Data & Analytics Engineer (m/f/d)

Job description

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? Your role Design, implement, and maintain backend services in Python that power our data access and distribution layer Build and operate automated build, test, and deployment pipelines following CI/CD and GitOps practices, running on AWS and Kubernetes Design and maintain data models and schemas (Avro, SQL) for our data pipelines and services, and publish them to downstream consumers via our Kafka-based distribution platform and schema registry Build access-layer data models in our analytics environment (e.g., Snowflake) so applications can work with the data directly, including via API Act as the coordination interface between the Data and Tech divisions, aligning priorities, schemas, and timelines across upstream and downstream teams Your profile 3+ years of experience in analytics engineering, data engineering, or a related role. Strong SQL skills and solid experience in data modeling (e.g., dimensional modeling, star schemas). Hands-on experience with event streaming and message distribution, ideally Kafka, including schema management with the Kafka schema registry (Avro, Protobuf, or JSON Schema). Experience designing data contracts and schemas between upstream producers and downstream consumers. Experience with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and modern data stack tools (e.g., dbt, CI/CD). Openness to AI-assisted development workflows (e.g., Claude Code or similar). Comfort working at the boundary of streaming/operational data and analytical data models, and building access layers that serve applicat…