Senior Data Engineer

Job Description

Who You'll Work With

You will be based in one of our technology hubs in either Waltham, MA or Dallas, TX as part of the broader McKinsey technology team (ClienTech).McKinsey technology fosters innovation driven by analytics, design thinking, mobile and social by developing new products/services and integrating them into our client work. It is helping to shift our model toward asset based consulting and is a foundation for and expands our investment in our entrepreneurial culture. Through innovative software as a service solutions, strategic acquisitions, and a vibrant ecosystem of alliances, we are redefining what it means to work with McKinsey. As one of the fastest-growing parts of our firm, McKinsey’s Technology team has more than 1,500 dedicated professionals (including more than 800 analysts and data scientists) and we’re hiring more mathematicians, data scientists, designers, software engineers, product managers, client development managers and general managers.

What You'll Do

You'll provide data engineering expertise in our client service. In this role, you will be responsible for coding and testing tools and assets to deliver high quality analytics to our customers. You will also perform the transformation, filtering and aggregation of raw data into concise, accurate and focused data marts or client specific data models. You may also be responsible for data-intensive ad-hoc analytics including novel analytics for which tools are not sufficiently push button. In addition, you may collaborate with the product and delivery team on the definition and requirements of analytics products within an agile framework.You'll be an integral part of our team with opportunities for coaching and mentoring from your senior colleagues.

Qualifications

• Bachelor’s degree in engineering, computer science or equivalent area
• 5+ years of experience in the field of business intelligence, application development, database development and ETL and/or data analysis domains with extensive SQL knowledge
• Experience with cloud infrastructure such as AWS, Azure or Google Experience in container technologies like Docker/Kubernetes a plus
• Experience in building the data pipelines and workflows from ground up including defining the exception handling strategies
• Exposure in storing and assembling large complex data sets with various formats such as CSV, JSON, Avro, Parquet
• Hands-on coding and application development experience with programming languages such as Python, Shell, Java or similar scripting languages
• Familiarity with R, Scala, Javascript is a big plus
• Experience with relational SQL and NoSQL databases and tools like Snaplogic, Alteryx, Tableau, Power BI
• Good understanding of REST APIs, data modeling, data warehousing, data lakes and big-data concepts
• Ability to translate business requirements; excellent time management skills (e.g. manage estimated workload according to scope)
• Demonstrated willingness and ability to engage with teams and collectively solve complex problems
• Proactively identify and advocate for improvements to technology and engineering methods
• Effectively communicate business benefits and implications of technology initiatives to non-technical and more senior colleagues