Data Analyst (m/f/d)

Job Description

Good storytelling starts with great listening. At Audible, that means each role and every project has our audience in mind. Because the same people who design, develop, and deploy our products also happen to use them. To us, that speaks volumes.
ABOUT THIS ROLE
Audible GmbH is looking for an enthusiastic Data Analyst (m/f/d) based in Berlin. You will be responsible for surfacing data-driven results and insights for the wider Analytics Team as well as the core marketing function. In this position you will provide operational, economic, and financial analysis to frame data driven decisions - and implement KPIs and scorecards to track initiative progress/performance to encourage a focus on results and execution. This position is initially open through a contract role lasting 12 months.
Working with various teams, you will be exposed to a wide range of functions - from engineering, data science and marketing – and develop the knowledge and skills to be a trusted analyst to the wider analytics team as well as, to our business stakeholders. You will be able to conduct multiple tasks simultaneously with support from leadership. Critical to the success of this role is your ability to develop insightful analysis, work effectively as part of a cross-functional team, and transform data to insight.
As a Data Analyst, you will...
· Build and maintain basic data artifacts (e.g., ETL, data models, queries) and reports that correctly answer business questions. Utilizes analytics and metrics visualization tools. Able to investigate data anomalies. Communicates effectively (e.g., proposals, findings, wikis).
· Provides solutions that inform team's business decisions.
· Identify trends in metrics. Able to segment metrics along suitable dimensions to reveal deeper dynamics.
· Dive deeply into technical and operational details of the business (e.g., key dependencies, business drivers/KPIs, develop actionable business insights, etc.) and contribute to constructive technical discussions.
· Improve dataset quality and automates manual processes.
· Be proficient in SQL. Know how to ingest process, persist, and analyze data. Basic understanding of distributed systems, data modelling, and scientific methods. Proficient in descriptive statistics and familiar with inferential statistics.