Resume Examples
July 02, 2026
19 Data Engineer Resume Examples, Backed by Real Interview Data (2026)
by Rennie HaylockData engineer resume examples from real resumes that reached interviews at Amazon, Figma, and Instacart, plus streaming and platform models.
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A data engineer resume is judged on one thing: did the pipelines stay up at scale. Not the stack you can name, but the rows you moved, the freshness you held, and the cost you cut while nothing broke downstream. The examples here show how real data engineers proved that on paper, from a first ingestion job to a 400 TB platform, and what the numbers say about the resumes that reached interviews.
What you see on this page traces back to 2,069 data engineer resumes stored in Huntr's system, not to generic advice. The lead example is an anonymized composite drawn from the 23 of those resumes that reached interviews, and it was verified against Huntr's research and reviewed by Sam Wright, Huntr's Head of Career Strategy. Names, employers, and schools are swapped so no example is a real person, and the labeled models draw on real data engineer postings.
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What Data Engineer Resumes That Reached Interviews Had in Common
We read 45 resumes from 221 people who landed data engineer interviews on Huntr, and counted the skills across 110,338 postings that list one. The figures in this section come from those two pulls.
- One data engineer here came up as a welder and an e-commerce quality-control lead, finished a full-stack program, and reached interviews on what the pipelines did, not the pedigree behind them. Most did not start as data engineers either; the common path is analyst, BI engineer, or DBA first.
- About 91% of the resumes we read open with a summary. The strong ones lead with scale in the first line: rows moved a day, pipeline uptime, or cost cut. If yours opens with adjectives, rewrite it.
- Python sits on 74% of the 110,338 postings that list skills, and SQL on 67%. If both are not on your resume, a screener may never reach the rest of it.
- ETL shows on 34% of postings and Spark in some form on roughly a third. Name the framework you actually ran, not the category word.
- Cloud is table stakes. AWS appears on 22% of postings and Azure on 11%, and the three certs people carried most were the AWS, Azure, and Google data engineer credentials.
- Orchestration and streaming separate mid from senior. Airflow and Kafka each land on about 12% of postings. The resumes that reached interviews named the DAGs they owned and the lag they held, not just the logos.
- The median resume ran about 1.8 pages, listed 4 jobs, and carried around 28 skills. Two pages is normal for a data engineer and reads better than a cramped one.
- Reliability is the story that sells. The resumes that landed interviews quantified uptime, freshness, and cost. The ones that listed tools with no outcomes read like a stack, not an engineer.
Data Engineer Resume Example That Reached Interviews
This composite mirrors real resumes from the interview set above. The name, employers, and schools are swapped for comparable real ones; the interview companies named are real.
Data Engineer Resume Example
Reached the interview stage
Built from real data engineer resumes that reached interviews at companies including Amazon, Figma, Notion, Instacart, Comcast, and Epic Games.
Grace Lin
Data Engineer - [email protected] - 111-111-1111 - Raleigh, NC - linkedin.com/in/grace-lin-example1 - github.com/grace-lin-example12
About
Data engineer with 3 years building ETL pipelines, data warehousing, and cloud workflows across banking and consulting. Built optimized SQL pipelines that cut query runtime by 60%, and ships AI-driven and analytics solutions in Python. Strong in SQL, Python, AWS, and Databricks, with a track record of translating business needs into scalable data products.
Experience
Data Engineer Associate
Cognizant (technology consulting)
11/2023 - Present
Raleigh, NC
- Designed, built, and maintained ETL workflows in Pentaho Data Integration to integrate and transform data from databases, flat files, and cloud platforms.
- Built optimized SQL queries using CTEs and window functions for extraction and transformation, improving query performance by 60%.
- Translated business requirements into technical specifications and worked directly with clients to deliver data solutions.
- Ran unit testing, regression testing, and QA validation to ensure data accuracy and reliable workflow execution.
- Collaborated with cross-functional and global teams to design, build, and deploy solutions aligned with business needs.
- Followed Agile practice, including sprint planning, daily stand-ups, and client demo sessions, for timely delivery.
Data Scientist Intern
Persistent Systems (software services)
09/2021 - 12/2021
Pune, India
- Built machine learning models in Python using TensorFlow and Scikit-learn for research and analysis tasks.
- Implemented computer vision techniques with OpenCV for image preprocessing, object detection, and feature extraction.
- Applied deep learning to image classification and object recognition problems.
- Preprocessed large datasets for training, ensuring data quality with Pandas and NumPy, and visualized results with Matplotlib and Seaborn.
Education
Certificate - Artificial Intelligence
University of North Carolina at Charlotte
Bachelor of Science - Information Technology
Northeastern University
Certifications
Databricks Certified Associate Developer
AWS Certified Cloud Practitioner
Microsoft Technology Associate, Introduction to Programming Using Python
Skills
Python • SQL • ETL & Data Warehousing • PySpark • Pentaho (PDI) • Azure Data Factory • Databricks • AWS • Query Optimization • CTEs & Window Functions • Data Modeling • Pandas • NumPy • Matplotlib • Seaborn • Git • Jira • Agile • QA & Unit Testing • Tableau • Power BI
Why it works: It reads as evidence, not inventory. Every role ties a tool to an outcome, and the one hard number in the summary, a 60% cut in query runtime, is the kind of claim a hiring manager can picture. See the methodology for how we build these.
The summary: Three lines with one measured claim. It names the domain (banking and consulting), the core tools (SQL, Python, AWS, Databricks), and the one number that matters: SQL pipelines that cut query runtime 60%. No wall of adjectives.
The experience: The bullets pair a tool with a result: ETL workflows built in Pentaho, CTEs and window functions that lifted query performance 60%, unit and regression testing for data accuracy, and business requirements turned into shipped data products. An earlier internship shows the ML and Python roots without pretending to be the main story.
The skills: A working stack, not a keyword dump: Python, SQL, ETL and data warehousing, PySpark, Azure Data Factory, Databricks, AWS, query optimization, and data modeling, with the BI tools that reporting teams expect. Every item is one a screener searching a data engineer posting would hit.
The rest of the examples are labeled models, built from Huntr's best-practice guidance and the skills real data engineer postings ask for, so you can see how each specialty reads on paper.
Entry-Level and Senior Data Engineer Resume Examples
Entry-Level Data Engineer Resume Example
Model built from posting data
A picture of a strong first data engineer resume, shaped by best practice and the skills junior data engineer postings name most.
Cora Lindqvist
Entry-Level Data Engineer - [email protected] - 111-111-1111 - Columbus, OH - linkedin.com/in/cora-lindqvist-example1 - github.com/cora-lindqvist-example12
About
Data engineer with 2 years building batch and API pipelines that now move about 40 million rows a day into a Snowflake warehouse. Started as a data analyst, moved into engineering, and now owns the ingestion jobs that reporting depends on. Comfortable in Python, SQL, and Airflow. Looking to grow on a team that treats data quality as a first-class problem.
Experience
Data Engineer
Northgate Analytics Group
08/2024 - Present
Columbus, OH
- Built 12 Airflow-orchestrated pipelines that load about 40M rows a day from 9 source systems into Snowflake, with alerting that pages on late loads.
- Cut a nightly batch job from 3 hours to 40 minutes by rewriting row-by-row Python into set-based SQL and partitioned loads.
- Added dbt tests on 60 critical models, which caught 3 upstream schema breaks before they reached the reporting layer.
- Wrote the runbook the on-call rotation now uses, dropping mean time to restore a failed load from 2 hours to 25 minutes.
- Project (github.com/cora-lindqvist-example12): an open-source CSV-to-Parquet loader with schema inference, used in 4 internal jobs.
Data Analyst
Beacon Retail Partners
06/2023 - 08/2024
Columbus, OH
- Wrote SQL and Python to prep weekly sales datasets for 15 stakeholders, which pushed me to automate the extracts and move into engineering.
- Built a Python script that replaced a manual 4-hour spreadsheet merge with a 5-minute scheduled job.
- Flagged a double-counting error in the revenue pipeline that had overstated one region by about 8%.
Education
Bachelor of Science - Information Systems
Ohio State University
09/2019 - 05/2023
Columbus, OH
Certifications
Microsoft Certified: Azure Data Engineer Associate (DP-203)
Skills
Python • SQL • Airflow • Snowflake • ETL • Data Modeling • dbt • AWS • S3 • Git • Pandas • Data Warehousing • Data Quality • PostgreSQL • Docker
What it shows: Two years can still carry scale. The summary claims 40 million rows a day into Snowflake, and the bullets prove the reliability habit early: 12 Airflow pipelines with paging alerts, a nightly job cut from 3 hours to 40 minutes, dbt tests that caught 3 schema breaks, and a runbook that dropped restore time from 2 hours to 25 minutes. The analyst-to-engineer jump is stated plainly, and a small open-source loader on GitHub stands in for the years a longer career would show.
Senior Data Engineer Resume Example
Model built from posting data
A model of platform-scale ownership, built on the tools and methods senior data engineer postings call for.
Desmond Achebe
Senior Data Engineer - [email protected] - 111-111-1111 - Austin, TX - linkedin.com/in/desmond-achebe-example1 - github.com/desmond-achebe-example12
About
Senior data engineer with 11 years owning the pipelines behind a 400 TB analytics platform. I keep the warehouse fresh, cheap, and trusted: 99.7% pipeline uptime across 200-plus jobs. Deep in Python, Spark, and Airflow on AWS. I lead by writing the hard jobs myself and reviewing the rest.
Experience
Senior Data Engineer
Meridian Data Systems
02/2021 - Present
Austin, TX
- Own 220 production pipelines feeding a 400 TB Snowflake and Redshift platform, holding 99.7% on-time delivery across the last 4 quarters.
- Cut warehouse compute spend by $310K a year by rewriting 18 heavy Spark jobs and moving cold data to a tiered storage layer.
- Led a migration from nightly batch to micro-batch, dropping data freshness from 6 hours to 20 minutes for 40 downstream teams.
- Set the data-contract and testing standard now used across 3 squads, which cut production data incidents by 55% year over year.
- Mentored 4 engineers; 2 were promoted within 18 months.
- Project (github.com/desmond-achebe-example12): a Spark job profiler that flags skewed partitions, adopted by the wider team.
Data Engineer
Trellis Software
05/2017 - 02/2021
Austin, TX
- Built the first Airflow deployment for the company, moving 70 cron jobs into monitored DAGs with retry and backfill.
- Designed a star schema for the finance mart that cut a key report from 90 seconds to 4 seconds.
- Reduced pipeline failures 40% by adding schema validation at ingest and dead-letter queues for bad records.
Data Engineer
Harbor Point Technologies
07/2014 - 05/2017
Dallas, TX
- Built ETL in Python and SQL to consolidate 6 legacy databases into one reporting warehouse.
- Automated a manual reconciliation process, saving the finance team about 20 hours a month.
- Tuned slow queries and indexes, cutting the overnight load window by 2.5 hours.
Education
Master of Science - Computer Science
University of Texas at Austin
09/2012 - 05/2014
Austin, TX
Bachelor of Engineering - Computer Engineering
University of Lagos
09/2008 - 06/2012
Lagos, NG
Certifications
AWS Certified Data Analytics - Specialty (DAS-C01)
Skills
Python • SQL • Apache Spark • Airflow • AWS • Snowflake • Data Modeling • ETL • Data Warehousing • Kafka • dbt • Terraform • Data Governance • CI/CD • Glue • Redshift • Data Quality • Scala
What it shows: Seniority here is reliability at scale, not a longer tool list. The summary leads with the number that matters, 99.7% uptime across 200-plus jobs on a 400 TB platform, and the bullets back it: $310K a year in compute cut, freshness dropped from 6 hours to 20 minutes for 40 teams, and a data-contract standard that cut incidents 55%. Mentoring shows as outcomes, with 2 of 4 engineers promoted, and a GitHub Spark profiler shows the work is hands-on.
Streaming and Platform Data Engineer Resume Examples
Streaming Data Engineer Resume Example
Model built from posting data
A model for real-time pipeline roles, grounded in the Kafka, Flink, and streaming skills that dominate these postings.
Camila Restrepo
Streaming Data Engineer - [email protected] - 111-111-1111 - Seattle, WA - linkedin.com/in/camila-restrepo-example1 - github.com/camila-restrepo-example12
About
Data engineer with 7 years building real-time pipelines that process about 2 billion events a day. I keep streaming systems honest: low lag, exactly-once where it matters, and clean recovery when a broker dies. Strong in Kafka, Spark Streaming, and Flink on AWS. I care most about the number nobody sees until it breaks, end-to-end latency.
Experience
Streaming Data Engineer
Vantage Streaming Labs
04/2022 - Present
Seattle, WA
- Run a Kafka and Flink platform that ingests about 2B events a day, holding p99 end-to-end latency under 800 ms.
- Cut consumer lag incidents 70% by repartitioning 5 hot topics and adding autoscaling on the Flink task managers.
- Built an exactly-once payment-events pipeline that reconciled to the penny across 3 downstream systems.
- Added a replay tool that lets the team reprocess a bad hour of data in under 15 minutes instead of a half-day rebuild.
- Wrote the Grafana dashboards and alerts the on-call team relies on, cutting time-to-detect a stalled stream to under 3 minutes.
- Project (github.com/camila-restrepo-example12): a Kafka lag exporter for Prometheus, used in the team's alerting.
Data Engineer
Cascade Metrics Inc.
06/2019 - 04/2022
Portland, OR
- Moved a batch clickstream pipeline to Kafka and Spark Streaming, dropping data freshness from 1 hour to 90 seconds.
- Built schema-registry-backed contracts for 30 event types, ending a recurring class of downstream parse failures.
- Reduced cloud cost 25% by right-sizing the streaming cluster and moving checkpoints to cheaper storage.
Data Analyst
Rainier Insights
07/2017 - 06/2019
Portland, OR
- Built SQL models and dashboards for the growth team, then automated the ingest that fed them, which pulled me into engineering.
- Wrote a Python job that pulled 12 marketing APIs into one warehouse table on a schedule.
Education
Bachelor of Science - Computer Science
University of Washington
09/2013 - 06/2017
Seattle, WA
Certifications
Confluent Certified Developer for Apache Kafka
Skills
Apache Kafka • Apache Flink • Spark Streaming • Python • Scala • SQL • AWS • Kinesis • Airflow • Data Modeling • Kubernetes • Docker • Grafana • Data Quality • Terraform • NoSQL
What it shows: Streaming has its own grammar and this resume speaks it: about 2 billion events a day with p99 latency under 800 ms, consumer-lag incidents cut 70% by repartitioning hot topics, an exactly-once payments pipeline that reconciled to the penny, and a replay tool that fixes a bad hour in under 15 minutes. It measures the thing nobody sees until it breaks, end-to-end latency.
Data Platform Engineer Resume Example
Model built from posting data
A model for infrastructure and lakehouse work, tailored to the Terraform, Databricks, and CI/CD skills platform postings list.
Dmitri Volkov
Data Platform Engineer - [email protected] - 111-111-1111 - Denver, CO - linkedin.com/in/dmitri-volkov-example1 - github.com/dmitri-volkov-example12
About
Data platform engineer with 9 years building the infrastructure other data engineers run on. I own the lakehouse, the orchestration layer, and the CI/CD that ships pipelines safely. Deep in Terraform, Databricks, and AWS. I measure my work in developer time saved and the cost per query going down.
Experience
Data Platform Engineer
Summit Cloud Data
01/2021 - Present
Denver, CO
- Own the Databricks lakehouse serving 6 data teams and about 300 monthly active users, with infrastructure fully in Terraform.
- Cut cloud data spend $480K a year through cluster policies, auto-termination, and moving 200 TB to Delta with Z-ordering.
- Built a self-serve pipeline template and CI/CD that dropped time to ship a new production job from 2 weeks to 2 days.
- Rolled out column-level access controls and lineage across 4 domains, closing an audit finding ahead of deadline.
- Reduced platform incidents 45% by adding automated integration tests that run against a staging lakehouse on every merge.
- Project (github.com/dmitri-volkov-example12): a Terraform module for governed Databricks workspaces, reused across 3 environments.
Data Engineer
Frontier Analytics Co.
08/2017 - 01/2021
Denver, CO
- Migrated an on-prem Hadoop cluster to AWS, cutting infrastructure cost 35% and ending weekend maintenance windows.
- Built the first CI/CD for the data team, replacing manual deploys with tested, versioned releases.
- Automated warehouse provisioning with Terraform, cutting new-environment setup from 3 days to 1 hour.
Database Administrator
Cedar Systems Group
06/2015 - 08/2017
Boulder, CO
- Managed 40 production databases, tuning queries and backups, which taught me the infrastructure side of data.
- Cut a recurring failover from 30 minutes to under 5 by scripting the process end to end.
Education
Master of Science - Information Technology
Colorado State University
09/2013 - 05/2015
Fort Collins, CO
Bachelor of Science - Computer Science
University of Colorado Boulder
09/2009 - 05/2013
Boulder, CO
Certifications
AWS Certified Solutions Architect - Professional
Databricks Certified Data Engineer Professional
Skills
AWS • Terraform • Databricks • Kubernetes • Python • SQL • Airflow • CI/CD • Apache Spark • Data Governance • Glue • Docker • Delta Lake • Data Modeling • Snowflake • DevOps • Data Quality
What it shows: This is the engineer other data engineers run on. The bullets are measured in developer time and cost: a Databricks lakehouse for 6 teams fully in Terraform, $480K a year in cloud spend cut, ship time for a new job dropped from 2 weeks to 2 days, and column-level access controls that closed an audit finding early. A DBA past explains where the infrastructure instinct came from.
Analytics and ML Data Engineer Resume Examples
Analytics Engineer Resume Example
Model built from posting data
A model for the dbt-and-warehouse side of the field, built around the SQL, dbt, and modeling skills analytics engineer postings name.
Corinne Bassett
Analytics Engineer - [email protected] - 111-111-1111 - Chicago, IL - linkedin.com/in/corinne-bassett-example1 - github.com/corinne-bassett-example12
About
Analytics engineer with 6 years turning raw tables into trusted models the business can actually use. I own about 300 dbt models feeding a Snowflake warehouse and the reporting layer on top. Strong in SQL, dbt, and Python. My job is that a metric means the same thing in every dashboard.
Experience
Analytics Engineer
Lakeshore Data Group
03/2022 - Present
Chicago, IL
- Own about 300 dbt models on Snowflake feeding 5 business teams, with tests and documentation on every core table.
- Built a single source of truth for revenue metrics, ending a running dispute where 3 dashboards showed 3 numbers.
- Cut warehouse cost 22% by consolidating redundant models and adding incremental builds on the largest tables.
- Added CI that runs dbt tests on every pull request, catching about 15 breaking changes before merge in the first quarter.
- Cut the finance team's monthly close prep from 3 days to 4 hours by modeling the ledger data they had been pulling by hand.
- Project (github.com/corinne-bassett-example12): a dbt package of reusable metric macros, adopted across 2 teams.
Data Analyst
Prairie Retail Analytics
06/2019 - 03/2022
Chicago, IL
- Built and maintained 40 SQL reports, then learned dbt to version and test them, which moved me into analytics engineering.
- Automated a weekly board deck data pull that had taken an analyst a full day every Monday.
- Cut a slow dashboard's load time from 45 seconds to 6 by rebuilding the underlying query and adding aggregates.
Education
Bachelor of Science - Statistics
University of Illinois at Chicago
09/2015 - 05/2019
Chicago, IL
Certifications
dbt Analytics Engineering Certification
Skills
SQL • dbt • Snowflake • Python • Data Modeling • Airflow • Data Warehousing • Looker • Tableau • Git • Data Quality • ETL • BigQuery • Data Governance • Power BI
What it shows: The job is trust in the numbers, and the bullets prove it: about 300 dbt models on Snowflake, a single source of truth that ended a fight where 3 dashboards showed 3 revenue figures, CI that caught 15 breaking changes before merge, and a monthly close cut from 3 days to 4 hours. Warehouse cost down 22% shows the modeling was efficient, not just correct.
ML Data Engineer Resume Example
Model built from posting data
A model for the machine-learning side of data engineering, shaped by the feature-store, Spark, and pipeline skills these postings require.
Devika Menon
ML Data Engineer - [email protected] - 111-111-1111 - San Jose, CA - linkedin.com/in/devika-menon-example1 - github.com/devika-menon-example12
About
Data engineer with 8 years building the pipelines and feature stores that machine learning teams depend on. I move data from raw events to model-ready features and keep training and serving in sync. Strong in Python, Spark, and Airflow on AWS. I own the plumbing so data scientists ship models, not fix pipelines.
Experience
ML Data Engineer
Northwind AI Data
05/2021 - Present
San Jose, CA
- Built the feature pipeline serving 25 production models, cutting feature freshness from 24 hours to 30 minutes.
- Ended training-serving skew for 2 top models by moving both paths to one shared feature store, lifting online accuracy.
- Built backfill tooling that generates 2 years of features for a new model in 3 hours instead of a week of manual runs.
- Cut GPU training idle time 40% by staging data ahead of jobs and caching hot feature sets in a faster store.
- Set up data validation on model inputs that caught 4 upstream drift events before they hit a live model.
- Project (github.com/devika-menon-example12): a lightweight point-in-time feature join library, used by 2 model teams.
Data Engineer
Willow Grove Systems
07/2018 - 05/2021
San Jose, CA
- Built Spark pipelines that prepared training datasets from about 500 GB of raw events a day.
- Partnered with data scientists to productionize 8 notebooks into scheduled, tested pipelines.
- Reduced a nightly feature job from 5 hours to 90 minutes by repartitioning and caching intermediate stages.
Data Analyst
Verde Data Co.
06/2016 - 07/2018
Sacramento, CA
- Built SQL datasets and dashboards for a marketing team, then started scripting the data prep, which led me into engineering.
- Automated a churn-report data pull that had taken 6 hours a week of manual work.
Education
Master of Science - Data Science
San Jose State University
09/2014 - 05/2016
San Jose, CA
Bachelor of Engineering - Information Technology
University of Pune
07/2010 - 05/2014
Pune, IN
Certifications
Google Certified Professional Data Engineer
Skills
Python • Apache Spark • SQL • Airflow • AWS • Feature Store • Kafka • Data Modeling • Snowflake • Kubernetes • Docker • MLflow • PySpark • Data Quality • Terraform • Delta Lake
What it shows: This resume owns the plumbing so data scientists ship models. It shows a feature pipeline serving 25 production models with freshness cut from 24 hours to 30 minutes, training-serving skew ended by one shared feature store, backfill tooling that builds 2 years of features in 3 hours, and input validation that caught 4 drift events before they hit a live model. The value is framed as scientist time returned, not notebooks run.
Big Data and AWS Data Engineer Resume Examples
Big Data Engineer Resume Example
Modeled from real big data postings
A model, not a tracked resume. It draws on the 1,812 big data engineer postings in Huntr's data, where employers like Rackspace, System Soft Technologies, and Anblicks hire, and leans on the Spark and Hadoop skills those roles ask for.
Terrence Okafor
Big Data Engineer - [email protected] - 111-111-1111 - Atlanta, GA - linkedin.com/in/terrence-okafor-example1 - github.com/terrence-okafor-example12
About
Big data engineer with 9 years running Spark and Hadoop workloads at petabyte scale. I keep batch and streaming jobs moving about 40 TB a day across a 200-node cluster and cut the cost of every run I own. Strong in Spark, Scala, and SQL. My work is measured in jobs that finish on time and clusters that do not fall over.
Experience
Big Data Engineer
Peachtree Data Systems
04/2021 - Present
Atlanta, GA
- Own about 40 TB a day of batch and streaming jobs on a 200-node Spark cluster feeding 6 analytics teams.
- Cut a core nightly pipeline from 6 hours to 90 minutes by repartitioning skewed joins and tuning shuffle.
- Lowered cluster cost 30% by right-sizing executors and moving cold data to a cheaper storage tier.
- Rebuilt a Hadoop ingestion path onto Spark Structured Streaming, dropping event latency from 15 minutes to under 1.
- Added data quality checks across 80 tables that caught about 20 bad loads before they reached reporting.
- Project (github.com/terrence-okafor-example12): a Spark partitioning helper that other teams reused on 3 pipelines.
Data Engineer
Blue Ridge Analytics
06/2017 - 04/2021
Atlanta, GA
- Built Hive and Spark jobs that processed about 5 TB a day of clickstream data.
- Cut storage cost 25% by converting raw JSON to Parquet and compacting small files.
- Automated a manual daily export that had taken an analyst 2 hours every morning.
Data Warehouse Analyst
Gwinnett Retail Group
08/2015 - 06/2017
Atlanta, GA
- Wrote SQL loads for a Teradata warehouse and started scripting them in Python, which moved me into engineering.
- Built the reporting tables behind a weekly sales dashboard used by 4 regional managers.
Education
Bachelor of Science - Computer Science
Georgia State University
09/2011 - 05/2015
Atlanta, GA
Certifications
Databricks Certified Associate Developer for Apache Spark
Skills
Apache Spark • Hadoop • Scala • Python • SQL • Hive • Kafka • HDFS • Airflow • AWS EMR • Data Modeling • Presto • Parquet • Delta Lake • Data Quality
What it shows: Scale is the whole story. The bullets count what the resume moved and kept up: about 40 TB a day on a 200-node cluster, a nightly pipeline cut from 6 hours to 90 minutes, cluster cost down 30%, and event latency dropped from 15 minutes to under 1. Quality checks across 80 tables show the jobs were trusted, not just fast.
AWS Data Engineer Resume Example
Built from AWS data engineer postings
A posting-modeled example with no interview claim. It reflects the 5,282 AWS-focused data engineer postings Huntr has seen from employers such as Tata Consultancy Services, CapTech, and Eliassen Group, and the Glue, Redshift, and Airflow skills they list.
Priya Ramaswamy
AWS Data Engineer - [email protected] - 111-111-1111 - Austin, TX - linkedin.com/in/priya-ramaswamy-example1 - github.com/priya-ramaswamy-example12
About
Data engineer with 7 years building pipelines on AWS. I run about 60 Glue and Spark jobs feeding a Redshift warehouse and keep the monthly cloud bill flat while volume grows. Strong in Python, SQL, and the AWS data stack. I treat cost and reliability as one problem, not two.
Experience
AWS Data Engineer
Colorado River Data
05/2021 - Present
Austin, TX
- Run about 60 Glue and Spark jobs feeding a Redshift warehouse for 5 product teams.
- Held the monthly AWS bill flat while data volume grew 3x, by moving batch jobs to spot capacity and adding S3 lifecycle rules.
- Cut a slow Redshift dashboard from 40 seconds to 5 by redesigning the sort and distribution keys.
- Built a Kinesis and Lambda path that lets the app team see events within 30 seconds instead of the next day.
- Moved 20 hand-run scripts into Airflow with alerting, ending the pages that used to wake me on weekends.
- Project (github.com/priya-ramaswamy-example12): a Terraform module for a standard Glue-to-Redshift pipeline, reused on 4 datasets.
Data Engineer
Lone Star Insights
07/2018 - 05/2021
Austin, TX
- Built S3 and Athena data lake tables that replaced a costly nightly database export.
- Cut a Spark on EMR job's runtime 50% by caching a reused lookup and tuning partitions.
- Set up CI that tested SQL models on every pull request, catching about 12 breaking changes in a quarter.
BI Developer
Hill Country Software
06/2016 - 07/2018
Austin, TX
- Built Tableau dashboards on a SQL warehouse, then automated the data prep, which led me into engineering.
Education
Bachelor of Technology - Computer Science
Anna University
07/2012 - 05/2016
Chennai, IN
Certifications
AWS Certified Data Engineer - Associate
Skills
AWS • Python • SQL • AWS Glue • Amazon Redshift • Amazon S3 • AWS Lambda • Amazon EMR • Airflow • Terraform • Apache Spark • Amazon Athena • Amazon Kinesis • Data Modeling • Snowflake
What it shows: This one frames cost and reliability together. It holds the AWS bill flat while volume grows 3x, cuts a Redshift dashboard from 40 seconds to 5, adds a Kinesis path that shows events in 30 seconds, and moves 20 scripts into Airflow with alerting. The Terraform module shows the work was repeatable, not one-off.
ETL Developer and Azure Data Engineer Resume Examples
ETL Developer Resume Example
Shaped by real ETL developer postings
This one is a model, marked as such. It follows the 707 ETL developer postings in Huntr's data from shops like Robert Half, HealthPlan Data Solutions, and Mastech Digital, and the Informatica, SSIS, and SQL skills they name.
Gregory Halvorsen
ETL Developer - [email protected] - 111-111-1111 - Minneapolis, MN - linkedin.com/in/gregory-halvorsen-example1 - github.com/gregory-halvorsen-example12
About
ETL developer with 10 years moving data between systems without losing a row. I own about 120 Informatica and SSIS workflows loading an enterprise warehouse every night. Strong in SQL, Informatica, and data modeling. I am the person who makes the 2 a.m. load finish clean.
Experience
ETL Developer
North Star Data Works
09/2019 - Present
Minneapolis, MN
- Own about 120 Informatica and SSIS workflows loading an enterprise warehouse every night for 8 business units.
- Cut the nightly load window from 7 hours to 3 by parallelizing sessions and pushing transforms into the database.
- Rebuilt error handling and restart logic that dropped failed-load reruns from weekly to about one a quarter.
- Migrated 40 legacy stored-procedure jobs to Informatica mappings with documented lineage.
- Added row-count and checksum validation across 60 loads that caught bad source files before reporting saw them.
- Project (github.com/gregory-halvorsen-example12): a reusable SSIS template package that standardized logging across 30 jobs.
ETL Developer
Great Lakes Systems
05/2015 - 09/2019
Minneapolis, MN
- Built Informatica mappings loading a Teradata warehouse from 15 source systems.
- Tuned slow sessions and cut an order-history load from 4 hours to 45 minutes.
- Wrote the stored procedures behind a finance reconciliation report used at month-end close.
Database Developer
Twin Cities Retail Co.
06/2013 - 05/2015
Minneapolis, MN
- Wrote SQL and T-SQL for a reporting database and started building SSIS packages, which moved me into ETL.
Education
Bachelor of Science - Information Systems
University of Minnesota
09/2009 - 05/2013
Minneapolis, MN
Certifications
Informatica Certified Professional
Skills
SQL • Informatica PowerCenter • SSIS • ETL • Data Warehousing • Oracle • T-SQL • Python • Talend • Data Modeling • Stored Procedures • Teradata • Control-M • Data Quality
What it shows: The value here is a load that finishes clean. It owns about 120 workflows, cuts the nightly window from 7 hours to 3, drops failed-load reruns from weekly to about one a quarter, and validates 60 loads before reporting sees them. Migrating 40 legacy jobs with documented lineage shows care for what came before.
Azure Data Engineer Resume Example
Modeled on Azure data engineer postings
A posting-modeled example, not a real cohort. It mirrors the 6,290 Azure data engineer postings Huntr has tracked at firms including Infosys, Tata Consultancy Services, and CapTech, and the Data Factory, Synapse, and Databricks skills those jobs require.
Nadia Kowalczyk
Azure Data Engineer - [email protected] - 111-111-1111 - Denver, CO - linkedin.com/in/nadia-kowalczyk-example1 - github.com/nadia-kowalczyk-example12
About
Azure data engineer with 8 years building pipelines on Data Factory, Synapse, and Databricks. I move about 30 TB a day into a lakehouse and hold report freshness under an hour for the business. Strong in PySpark, SQL, and the Azure data stack. I build so the dashboards are right before anyone asks.
Experience
Azure Data Engineer
Front Range Data Group
06/2021 - Present
Denver, CO
- Move about 30 TB a day into a Synapse and Databricks lakehouse for 6 reporting teams.
- Hold report freshness under an hour, down from a next-morning batch, by moving to incremental Data Factory pipelines.
- Cut Synapse compute cost 28% by pausing idle pools and rewriting 3 heavy queries.
- Built a Delta Lake medallion layout that ended duplicate logic across 4 teams' pipelines.
- Added data quality rules in Databricks that caught about 18 upstream schema changes before they broke a dashboard.
- Project (github.com/nadia-kowalczyk-example12): an Azure Data Factory pipeline template with built-in logging, reused on 5 sources.
Data Engineer
Mile High Analytics
07/2018 - 06/2021
Denver, CO
- Built Azure Data Factory pipelines loading a SQL warehouse from 12 sources.
- Cut a nightly PySpark job from 3 hours to 50 minutes by repartitioning and caching.
- Moved reporting off a strained production database onto a dedicated Synapse pool.
Data Analyst
Cherry Creek Software
08/2016 - 07/2018
Denver, CO
- Built Power BI reports on a SQL database, then automated the refresh, which led me into engineering.
Education
Bachelor of Science - Computer Science
University of Denver
09/2012 - 05/2016
Denver, CO
Certifications
Microsoft Certified: Azure Data Engineer Associate (DP-203)
Skills
Azure • Azure Data Factory • Azure Synapse • Databricks • PySpark • SQL • Python • Azure Data Lake • Delta Lake • Airflow • Data Modeling • Power BI • T-SQL • Data Governance
What it shows: Freshness and cost carry this one. It moves about 30 TB a day, holds report freshness under an hour, cuts Synapse cost 28%, and catches about 18 schema changes before a dashboard breaks. The medallion layout shows the engineer thought in shared layers, not one-off pipelines.
Data Warehouse Engineer and Snowflake Data Engineer Resume Examples
Data Warehouse Engineer Resume Example
Drawn from data warehouse engineer postings
A posting-modeled example, not a real person. It reflects the 26,703 data warehouse data engineer postings Huntr has tracked at firms like Synechron, Robert Half, and Insight Global, and the dimensional modeling, dbt, and Snowflake skills those roles ask for.
Marcus Delaney
Data Warehouse Engineer - [email protected] - 111-111-1111 - Austin, TX - linkedin.com/in/marcus-delaney-example1 - github.com/marcus-delaney-example12
About
Data warehouse engineer with 9 years building and modeling warehouses in Snowflake and Redshift. I rebuilt a star schema that cut median report query time from 40 seconds to under 4 and now serve 200 analysts from one governed model. Strong in dbt, SQL, and dimensional design.
Experience
Senior Data Warehouse Engineer
Lone Star Data Systems
05/2020 - Present
Austin, TX
- Rebuilt the core warehouse on Snowflake with a Kimball star schema, cutting median report query time from 40 seconds to under 4.
- Model and maintain 60 dbt models that about 200 analysts query daily.
- Cut warehouse spend 22% by adding cluster keys and right-sizing warehouses.
- Set dimensional standards that ended 3 conflicting revenue definitions across teams.
- Built dbt tests that caught about 25 breaking source changes before month-end close.
- Project (github.com/marcus-delaney-example12): a dbt macro pack for slowly changing dimensions, reused on 4 marts.
Data Warehouse Developer
Hill Country Analytics
06/2016 - 05/2020
Austin, TX
- Built ETL into a Redshift warehouse from 15 sources with Informatica and SQL.
- Cut a nightly load from 5 hours to 90 minutes by rewriting joins and adding sort keys.
- Modeled the first shared finance mart, replacing 6 spreadsheet reports.
Data Analyst
Barton Springs Software
07/2014 - 06/2016
Austin, TX
- Wrote SQL reports on a warehouse and built the first sales dashboard, which led me into engineering.
Education
Bachelor of Science - Information Systems
University of Texas at Austin
09/2010 - 05/2014
Austin, TX
Certifications
Snowflake SnowPro Core Certification
Skills
Data Warehousing • Dimensional Modeling • SQL • Python • ETL • Snowflake • dbt • Airflow • Data Modeling • Redshift • Informatica • Kimball • Star Schema • Tableau
What it shows: Modeling discipline. The star-schema rebuild cut report query time from 40 seconds to under 4, the dbt tests caught about 25 breaking changes, and one governed model now serves 200 analysts. Warehouse spend still dropped 22%.
Snowflake Data Engineer Resume Example
Based on real Snowflake data engineer postings
This one is modeled from postings, not a live candidate. It tracks the 22,194 Snowflake data engineer roles Huntr has seen at employers such as Insight Global, Synechron, and Anblicks, and the dbt, SQL, and cost-tuning work those jobs expect.
Ingrid Solvang
Snowflake Data Engineer - [email protected] - 111-111-1111 - Minneapolis, MN - linkedin.com/in/ingrid-solvang-example1 - github.com/ingrid-solvang-example12
About
Snowflake data engineer with 7 years running warehouse and lakehouse workloads on Snowflake. I cut monthly Snowflake spend 31% while moving about 12 TB a day, and I keep a 90-model dbt project building under 20 minutes so analysts start with fresh data. Strong in dbt, SQL, and Snowpark.
Experience
Snowflake Data Engineer
North Loop Data
07/2021 - Present
Minneapolis, MN
- Move about 12 TB a day into Snowflake with Fivetran and custom Python loaders.
- Cut monthly Snowflake spend 31% by tuning warehouse sizes, auto-suspend, and query pruning.
- Keep a 90-model dbt project building under 20 minutes with incremental models.
- Built Snowpark pipelines that replaced 3 external Spark jobs.
- Added dbt tests and freshness checks that caught about 20 bad loads before dashboards updated.
- Project (github.com/ingrid-solvang-example12): a Snowflake cost dashboard built on ACCOUNT_USAGE, adopted by 3 teams.
Data Engineer
Mill City Analytics
06/2018 - 07/2021
Minneapolis, MN
- Built the first Snowflake warehouse, migrating off a strained SQL Server.
- Loaded 18 sources with Airflow and cut reporting lag from a day to under an hour.
Data Analyst
Stone Arch Software
08/2016 - 06/2018
Minneapolis, MN
- Wrote SQL reporting and automated the weekly refresh, which moved me into engineering.
Education
Bachelor of Science - Statistics
University of Minnesota
09/2012 - 05/2016
Minneapolis, MN
Certifications
SnowPro Advanced: Data Engineer
Skills
Snowflake • SQL • Python • dbt • ETL • AWS • Airflow • Spark • Data Modeling • Kafka • Data Warehousing • Snowpark • Fivetran • CI/CD
What it shows: Cost and freshness together. It moves about 12 TB a day, cuts Snowflake spend 31%, and keeps a 90-model dbt build under 20 minutes. The ACCOUNT_USAGE cost dashboard shows the engineer watches the bill, not just the pipeline.
Spark Data Engineer and Databricks Data Engineer Resume Examples
Spark Data Engineer Resume Example
Patterned on Spark data engineer postings
A composite built from postings rather than one hire. It mirrors the 41,730 Spark-heavy data engineer roles Huntr has tracked at companies including Tata Consultancy Services, Synechron, and Insight Global, and the PySpark, Scala, and Kafka skills they list.
Rafael Ortega
Spark Data Engineer - [email protected] - 111-111-1111 - San Jose, CA - linkedin.com/in/rafael-ortega-example1 - github.com/rafael-ortega-example12
About
Spark data engineer with 8 years writing batch and streaming jobs in PySpark and Scala. I cut a core Spark pipeline from 3 hours to 35 minutes and process about 50 TB a day on EMR without blowing the budget. Strong in Spark tuning, Kafka, and Delta Lake.
Experience
Senior Spark Data Engineer
Bay Area Data Works
04/2020 - Present
San Jose, CA
- Process about 50 TB a day in PySpark on EMR for fraud and reporting teams.
- Cut a core Spark job from 3 hours to 35 minutes by fixing skew and repartitioning.
- Cut EMR cost 27% with spot instances and right-sized clusters.
- Moved 6 batch jobs to Structured Streaming on Kafka, dropping latency from hours to minutes.
- Built a shared Scala library for schema handling, reused across 10 jobs.
- Project (github.com/rafael-ortega-example12): a PySpark skew-diagnosis notebook the team runs on slow jobs.
Data Engineer
Silicon Valley Analytics
07/2017 - 04/2020
San Jose, CA
- Built Spark ETL on Hadoop and Hive loading a warehouse from clickstream logs.
- Tuned partitioning that cut a daily job from 4 hours to 70 minutes.
Software Engineer
Guadalupe Systems
07/2015 - 07/2017
San Jose, CA
- Built JVM backend services, then moved into the data platform team.
Education
Bachelor of Science - Computer Engineering
San Jose State University
09/2011 - 05/2015
San Jose, CA
Certifications
Databricks Certified Associate Developer for Apache Spark
Skills
Apache Spark • PySpark • Scala • Python • SQL • Kafka • Hadoop • AWS • Airflow • Databricks • Delta Lake • Data Modeling • Hive • EMR
What it shows: Tuning depth. It cuts a core job from 3 hours to 35 minutes, processes about 50 TB a day, and trims EMR cost 27%. Fixing skew and repartitioning are the kind of specifics interviewers probe.
Databricks Data Engineer Resume Example
Modeled from Databricks data engineer postings
An illustrative example, not a real cohort. It follows the 17,539 Databricks data engineer postings Huntr has logged at firms like Insight Global, Neudesic, and Harnham, and the Delta Lake, Unity Catalog, and PySpark work those roles center on.
Hannah Brenner
Databricks Data Engineer - [email protected] - 111-111-1111 - Chicago, IL - linkedin.com/in/hannah-brenner-example1 - github.com/hannah-brenner-example12
About
Databricks data engineer with 7 years building lakehouse pipelines on Databricks and Delta Lake. I run a medallion architecture that moves about 20 TB a day and cut job cost 30% by moving to job clusters and Photon. Strong in PySpark, Unity Catalog, and CI/CD.
Experience
Databricks Data Engineer
Lakeshore Data Group
05/2021 - Present
Chicago, IL
- Run a Delta Lake medallion lakehouse moving about 20 TB a day for 5 teams.
- Cut job cost 30% by switching to job clusters, Photon, and autoscaling.
- Set up Unity Catalog governance across 3 workspaces, ending shared-token access.
- Cut a nightly PySpark job from 2 hours to 40 minutes by tuning shuffle and caching.
- Added Delta expectations that caught about 15 schema drifts before a dashboard broke.
- Project (github.com/hannah-brenner-example12): a Databricks Asset Bundle template for CI/CD, reused on 6 pipelines.
Data Engineer
Loop Analytics
06/2018 - 05/2021
Chicago, IL
- Built Azure Data Factory and Databricks pipelines feeding a Synapse warehouse.
- Migrated 12 legacy notebooks into version-controlled, scheduled jobs.
Data Analyst
Wacker Software
08/2016 - 06/2018
Chicago, IL
- Built reporting on a SQL warehouse and automated refreshes, which led me into engineering.
Education
Bachelor of Science - Computer Science
University of Illinois at Urbana-Champaign
09/2012 - 05/2016
Urbana, IL
Certifications
Databricks Certified Data Engineer Professional
Skills
Databricks • PySpark • Spark • Delta Lake • Python • SQL • Azure Data Factory • Azure • AWS • Airflow • Unity Catalog • Data Modeling • Snowflake • CI/CD
What it shows: Lakehouse fluency. A medallion layout moves about 20 TB a day, job cost drops 30% with Photon and job clusters, and Unity Catalog governance replaces shared tokens. The Asset Bundle template shows real CI/CD habits.
GCP Data Engineer and Hadoop Data Engineer Resume Examples
GCP Data Engineer Resume Example
Shaped by GCP data engineer postings
Built from job postings, not a single career. It reflects the 11,401 GCP data engineer roles Huntr has tracked at employers such as Insight Global, CapTech, and Compunnel, and the BigQuery, Dataflow, and Composer skills they require.
Yusuf Demir
GCP Data Engineer - [email protected] - 111-111-1111 - Seattle, WA - linkedin.com/in/yusuf-demir-example1 - github.com/yusuf-demir-example12
About
GCP data engineer with 8 years building pipelines on BigQuery, Dataflow, and Cloud Composer. I held BigQuery spend flat while data tripled and keep 40 Airflow DAGs green so analysts trust the numbers. Strong in Apache Beam, Terraform, and cost tuning.
Experience
GCP Data Engineer
Emerald City Data
04/2020 - Present
Seattle, WA
- Move data into BigQuery with Dataflow and Pub/Sub for 6 product teams.
- Held BigQuery spend flat while volume tripled by partitioning, clustering, and slot reservations.
- Run 40 Cloud Composer DAGs, keeping on-time loads above 99%.
- Cut a Dataflow streaming job's cost 25% by tuning windowing and worker counts.
- Manage GCP data infrastructure as Terraform, ending click-ops drift across 3 projects.
- Project (github.com/yusuf-demir-example12): a BigQuery cost-audit query pack, adopted by 4 teams.
Data Engineer
Puget Sound Analytics
07/2017 - 04/2020
Seattle, WA
- Built the first BigQuery warehouse, migrating off nightly CSV exports.
- Loaded 20 sources with Airflow and cut reporting lag to under an hour.
Data Analyst
Pike Place Software
08/2015 - 07/2017
Seattle, WA
- Wrote SQL reports and built the first shared dashboard, which led me into engineering.
Education
Bachelor of Science - Applied Mathematics
University of Washington
09/2011 - 05/2015
Seattle, WA
Certifications
Google Cloud Professional Data Engineer
Skills
GCP • BigQuery • Dataflow • Python • SQL • Apache Beam • Cloud Composer • Airflow • Pub/Sub • dbt • Spark • Data Modeling • Terraform • Dataproc
What it shows: Spend control at scale. BigQuery cost stays flat while data triples, 40 Composer DAGs stay green, and infrastructure lives in Terraform. Partitioning and slot reservations are named, not hand-waved.
Hadoop Data Engineer Resume Example
Grounded in Hadoop data engineer postings
A posting-modeled resume, not a real applicant. It draws on the 12,713 Hadoop-focused data engineer roles Huntr has seen at companies including Tata Consultancy Services, Synechron, and Apexon, and the Hive, Spark, and Kafka skills those jobs name.
Anika Deshmukh
Hadoop Data Engineer - [email protected] - 111-111-1111 - Charlotte, NC - linkedin.com/in/anika-deshmukh-example1 - github.com/anika-deshmukh-example12
About
Hadoop data engineer with 9 years running large batch pipelines on Hadoop, Hive, and Spark. I tuned a Hive workload that cut a 6-hour job to 90 minutes and keep a 300-node cluster feeding 8 downstream teams. Strong in HDFS tuning, Sqoop, and Spark migration.
Experience
Senior Hadoop Data Engineer
Queen City Data Systems
06/2019 - Present
Charlotte, NC
- Run batch pipelines on a 300-node Hadoop cluster feeding 8 downstream teams.
- Cut a core Hive job from 6 hours to 90 minutes with partitioning and ORC files.
- Moved 5 MapReduce jobs to Spark, cutting runtime about 60%.
- Built Kafka-to-HDFS ingestion holding intraday freshness for risk reporting.
- Set retention and small-file compaction that recovered about 40 TB of HDFS.
- Project (github.com/anika-deshmukh-example12): an Oozie-to-Airflow migration script, reused on 20 workflows.
Data Engineer
Carolina Analytics
07/2015 - 06/2019
Charlotte, NC
- Built Sqoop and Hive ETL loading a warehouse from 25 relational sources.
- Tuned Hive queries that cut a nightly load from 5 hours to under 2.
Software Engineer
Catawba Software
07/2013 - 07/2015
Charlotte, NC
- Built backend services in Java, then moved into the data platform team.
Education
Bachelor of Science - Computer Science
North Carolina State University
09/2009 - 05/2013
Raleigh, NC
Certifications
Cloudera CCA Spark and Hadoop Developer
Skills
Hadoop • Hive • Spark • Scala • Python • SQL • Kafka • HDFS • MapReduce • Sqoop • Oozie • Data Modeling • HBase • Impala
What it shows: Big-cluster experience. It cuts a 6-hour Hive job to 90 minutes, moves MapReduce to Spark for about a 60% gain, and recovers 40 TB with compaction. The Oozie-to-Airflow script shows the engineer modernizes, not just maintains.
Lead Data Engineer and Data Infrastructure Engineer Resume Examples
Lead Data Engineer Resume Example
Informed by lead data engineer postings
This example comes from postings, not one manager. It maps to the 4,896 lead data engineer roles Huntr has tracked at employers such as Capital One, CapTech, and Harnham, where the job blends deep pipeline work with team leadership.
Theodore Novak
Lead Data Engineer - [email protected] - 111-111-1111 - Boston, MA - linkedin.com/in/theodore-novak-example1 - github.com/theodore-novak-example12
About
Lead data engineer with 11 years, the last 4 leading a team of 6. I set the platform roadmap that cut pipeline incidents 45% and shipped a self-serve model that took analyst data requests from days to hours. Strong in Snowflake, dbt, Airflow, and mentoring.
Experience
Lead Data Engineer
Charles River Data
03/2020 - Present
Boston, MA
- Lead a team of 6 building pipelines on Snowflake, dbt, and Airflow.
- Set a reliability roadmap that cut pipeline incidents 45% in a year.
- Shipped a self-serve data model that cut analyst request turnaround from days to hours.
- Introduced code review and CI/CD, ending direct pushes to production.
- Run hiring and mentoring; promoted 2 engineers to senior.
- Project (github.com/theodore-novak-example12): a pipeline SLA framework the team uses to triage on-call.
Senior Data Engineer
Back Bay Analytics
06/2016 - 03/2020
Boston, MA
- Built the core Airflow platform and migrated 40 cron jobs onto it.
- Cut a nightly Spark pipeline from 3 hours to 50 minutes.
Data Engineer
Fenway Software
07/2013 - 06/2016
Boston, MA
- Built ETL into a warehouse and owned the reporting pipeline for 3 teams.
Education
Bachelor of Science - Computer Science
Boston University
09/2009 - 05/2013
Boston, MA
Certifications
AWS Certified Solutions Architect - Associate
Skills
Data Engineering • Python • SQL • Spark • Airflow • Snowflake • dbt • AWS • Kafka • Data Modeling • CI/CD • Mentoring • Roadmapping • Stakeholder Management
What it shows: Leadership backed by numbers. Pipeline incidents fall 45%, analyst request time drops from days to hours, and 2 engineers get promoted. The roadmap and on-call framework show scope beyond writing code.
Data Infrastructure Engineer Resume Example
Modeled on data infrastructure engineer postings
An illustrative build, not a real hire. It reflects the 14,738 data engineer roles emphasizing infrastructure that Huntr has tracked at firms like State Farm, Brooksource, and Harnham, and the Kubernetes, Terraform, and CI/CD skills they lean on.
Fiona Gallagher
Data Infrastructure Engineer - [email protected] - 111-111-1111 - Portland, OR - linkedin.com/in/fiona-gallagher-example1 - github.com/fiona-gallagher-example12
About
Data infrastructure engineer with 8 years running the platform data teams build on. I moved batch and streaming workloads onto Kubernetes, cut infrastructure cost 33%, and kept pipeline uptime at 99.9% across 3 regions. Strong in Terraform, Helm, and CI/CD.
Experience
Data Infrastructure Engineer
Willamette Data Group
05/2020 - Present
Portland, OR
- Run Airflow, Spark, and Kafka on Kubernetes for 7 data teams across 3 regions.
- Cut infrastructure cost 33% with autoscaling, spot nodes, and right-sized requests.
- Manage all data infrastructure as Terraform and Helm, ending manual cluster changes.
- Held pipeline uptime at 99.9% with Prometheus alerts and on-call runbooks.
- Built a CI/CD pipeline that cut deploy time from an hour to 8 minutes.
- Project (github.com/fiona-gallagher-example12): a Helm chart for self-serve Airflow, adopted by 4 teams.
Data Engineer
Rose City Analytics
06/2017 - 05/2020
Portland, OR
- Containerized 15 batch jobs and moved them off aging VMs.
- Built the first Terraform modules for the data warehouse stack.
DevOps Engineer
Hawthorne Software
07/2015 - 06/2017
Portland, OR
- Ran CI/CD and cloud infrastructure, then moved onto the data platform team.
Education
Bachelor of Science - Computer Science
Oregon State University
09/2011 - 05/2015
Corvallis, OR
Certifications
Certified Kubernetes Administrator (CKA)
Skills
Kubernetes • Terraform • Docker • Python • AWS • Airflow • Spark • CI/CD • Kafka • Data Modeling • SQL • Helm • Prometheus • Snowflake
What it shows: Platform reliability. Workloads run on Kubernetes across 3 regions at 99.9% uptime, infrastructure cost falls 33%, and deploys drop from an hour to 8 minutes. Terraform and Helm show everything is code, not clicks.
Skills for a Data Engineer Resume
We counted skills across 110,338 real data engineer postings on Huntr that list one. Build your list from what employers actually ask for, then keep only what the specific posting names.
Core languages: Python (74% of postings), SQL (67%), Java (18%), Scala (14%). Python and SQL are non-negotiable; a JVM language helps for Spark-heavy shops. Name the ones you have shipped production code in.
Pipelines and processing: ETL (34%), Spark in some form (about a third combined), data modeling (about 25%), Airflow (12%), Kafka (12%), data warehousing (about 17%). This is where reliability lives. Pair each with a number: rows moved, freshness held, jobs orchestrated.
Cloud and tools: AWS (22%), Snowflake (19%), Databricks (15%), Azure (11%) plus Azure Data Factory (8%), Hadoop (11%), Kubernetes (8%), Docker (7%), Terraform (6%). Cloud is table stakes; list the platform you actually operated in, not every logo you have touched.
How to use this: The interview-winning resumes carried a median of about 28 skills, spread across languages, pipeline tools, and cloud, and tailored per posting. A list of tools with no core languages, or languages with no orchestration, reads half-built.
Action Verbs for Data Engineer Resumes
Build: built, designed, orchestrated, shipped, productionized. Use these where a scale figure can follow: built 12 pipelines moving 40M rows a day.
Reliability: held, recovered, validated, monitored, backfilled. These carry the on-call story: held 99.7% uptime across 200 jobs; recovered a bad hour of data in under 15 minutes.
Optimize: cut, tuned, migrated, consolidated, right-sized. Pair with money or time: cut warehouse spend $310K a year; migrated 340K rows with zero data loss.
The weak version of every data engineer bullet starts with "responsible for." The strong version starts with what you built and ends with the number that stayed up.
Turn a Weak Data Engineer Bullet Into a Strong One
Weak
Responsible for building and maintaining ETL pipelines and data warehouses using Python, SQL, and various cloud tools.
Strong
Built 12 Airflow pipelines that move 40M rows a day into Snowflake at 99.5% on-time delivery, and cut the nightly batch from 3 hours to 40 minutes by moving row-by-row Python to set-based SQL.
Same job, different evidence. The strong bullet answers the three questions a data hiring manager has: how much data, how reliably, and did you make it faster or cheaper.
Data Engineer vs Data Scientist on a Resume
Scope: a data engineer builds and runs the pipelines and warehouses that move data; a data scientist uses that data to build models and answer questions. Metrics: a data engineer resume proves uptime, freshness, throughput, and cost; a data scientist resume proves model accuracy, lift, and a business result tied to it. Overlap: both carry Python and SQL, so the difference lands in the bullets. If yours are about pipelines that stayed up, you are an engineer; if they are about models that moved a number, look at the data scientist page.
Getting a Data Engineer Resume Through the Screener
A screener matches strings before a human reads a word. Keep the title standard (Data Engineer, not Data Wizard), spell tools the way postings spell them (Apache Spark and PySpark are different strings, so is Airflow versus workflow orchestration), and mirror the posting's exact stack, since Snowflake, Kafka, and dbt are literal matches, not concepts. Run the posting through Huntr's keyword scanner to see the terms you are missing, then let Resume Tailor work the matches into your bullets. Tailored resumes convert to interviews at about twice the rate of generic ones in our data.
Data Engineer Resume FAQ
How long should a data engineer resume be?
About two pages. The interview-stage resumes we read ran a median of roughly 1.8 pages with 4 jobs. Two pages is normal for a data engineer and reads better than a cramped one page. Cut filler, never cut a pipeline you built and ran.
Do I need a certification to get data engineer interviews?
No, but the right one signals cloud fluency. The certs people carried most were the AWS, Azure (DP-203), and Google data engineer credentials, matching the cloud share in the postings. A cert helps a screener; shipped, reliable pipelines close the interview.
How do I write a data engineer resume coming from a data analyst or DBA role?
Reframe the data work you already did. Most people in this set came up as analysts, BI engineers, or DBAs; one came from welding and e-commerce quality control. Write the bullets where you automated an extract, moved data at scale, or made a pipeline faster, and name the tools (Python, SQL, Airflow) so a screener catches them.
What skills should a data engineer put on a resume?
Start from the posting. Across 110,338 data engineer postings the constants are Python (74%) and SQL (67%), then ETL, Spark, data modeling, and a cloud platform (AWS, Snowflake, Databricks, or Azure), with Airflow and Kafka separating mid from senior. The interview-winning resumes listed a median of about 28 skills, tailored per application.
Methodology
The verified example is a composite of real resumes attached to jobs that reached the interview stage for data engineer roles on Huntr, drawn from a cohort of 221 people and 45 interview-stage resumes. We swap names, employers, and schools for comparable real ones and check every swap against our database so no example points to a real person. Where figures are blended we keep them as written; where one resume anchors a number we shift it to a nearby value. The interview companies we name are real and never changed.
The specialty examples are models, marked as such on every callout. They make no interview claim. Their skills come from the 110,338-posting count cited above, and their shape follows what the verified resume does: one measured claim in the summary, a number in every bullet, and tools named the way a data engineer posting names them.
Conclusion
Across the interview-stage resumes we read, the pattern held: one measured claim up top, bullets that count what you moved and how reliably you moved it, cloud and orchestration tools named the way postings name them, and a length that respects the work. None of it needs a perfect pedigree or a title that already says data engineer. It needs pipelines that stayed up and numbers that prove it.
Build yours in Huntr's resume builder, then run each application through Resume Tailor so your stack matches the posting before a screener ever sees it.
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