Data Engineer - Data Platform (m/f/d)

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Additional Information

At Statista , we're all about facts and data, for we are the world's leading business data platform. By providing reliable and easy-to-use data as well as various data analytics products and services, we empower people worldwide to make fact-based decisions. Founded in Hamburg in 2007, we have quickly grown into a global company with offices in major cities such as London, New York, Berlin and Tokyo. And we still have a lot of plans. Our constant growth does not only prove our success, but also keeps creating new development and career opportunities for our employees. We value and celebrate our diverse culture. You are welcome here for who you are, no matter where you come from, what you look like, or whether you prefer bar graphs to pie charts. Your story matters - keep writing it as part of our team. Are you ready to join us? As a Data Engineer on our Statista R Data Platform, you'll own data ingestion and platform reliability end-to-end: designing and operating scalable batch pipelines, hardening orchestration, ensure data quality, security, and cost efficiency. You'll partner closely with Data Analysts, Software Developers and Data Scientists to deliver well-governed datasets tailored for the teams' specific need and maturity to make high-quality data accessible and trustworthy for analysis. Your role - Develop and optimize ELTs/ ETLs to integrate new data sources and enhance data management in the data lake and other systems - Develop reliable data orchestration workflows to ensure and support the ranking creation process of Statista R's analyst teams. - Drive cross-functional collaboration with engineering, data science, and analysis teams to deliver documented, reproducible datasets that power rankings, KPIs and business decisions. - Monitor and optimize data infrastructure to ensure high availability, performance, and security Your profile - Bachelor's or master's in computer science, Data Science, Engineering, or related field. - 2+ years in data engineering building production pipelines and platforms; including a sustained period within one organization seeing a product through build → launch → iteration - Comfortable in developing ELT/ETL processes and data automation processes using Python and SQL - Proficiency in workflow orchestration, containerization and version control tools (e.g. Apache Airflow, Docker or git) - Good understanding of data modelling and architecture, with a strong interest in best practices - Knowledge in dlt, dbt, Snowflake, infrastructure as code and software development best practices is a plus - Hands-on experience in cloud services and infrastructure (e.g. AWS: S3, Glue, Athena, ECS, RDS)

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