Data Engineer

Deine Rolle im Team

Unsere Erwartungen an dich

Qualifikationen

• Must have:
• Python - strong, idiomatic, tested. You write pipelines that other people can read and easily debug.
• SQL - advanced. Window functions, CTEs, query plans, and the instinct to know why a query got slow.
• Data modeling & warehousing - dimensional modeling, incremental vs. full-refresh strategies, slowly changing dimensions, and the trade-offs between them.
• dbt - building, testing, and documenting models in a real project.
• Apache Airflow - authoring DAGs, plus the operational side: backfills, retries, SLAs, and debugging a failed run.
• Docker & Kubernetes - you can containerize a job and understand what happens when a pod won't schedule.
• Git and CI/CD - branching, code review, and pipelines that gate merges.
• Nice to have:
• Knowledge of ClickHouse or another columnar OLAP engine (BigQuery, Redshift) table engines, partitioning, and MergeTree tuning are a big plus.
• Familiarity with infrastructure as code (IaC) - Terraform, Helm, Ansible.
• Working knowledge of Apache Spark - PySpark or Scala.
• Soft skills:
• Attention to detail - You care about correctness and you build the checks that prove it. In data engineering, a silently wrong number is worse than a loud failure.
• Ownership - you run what you build, and you improve it when needed.
• Clear communication - you can effectively explain in a pipeline to an analyst and a trade-off to a stakeholder.
• Pragmatism - you can tell the difference between the right long-term design and the short-term solution, and you know when each one applies.
• Collaboration - you work well with data engineers, analysts, and other product and backend teams.
• Language requirements:
• English - fluent, written and spoken.

Erfahrung

• We're looking for someone with data engineering experience, who is dedicated to creating exceptional user experiences and driving innovation.
• 4+ years building and running production data pipelines.
• AWS - hands-on experience with S3 and the surrounding data services (Athena, Kinesis, EKS, Lambda).
• Good experience with streaming data ingestion technologies such as Kafka, Kinesis, or similar.
• Experience with data lake architectures - Parquet, Iceberg, or Delta Lake, and an understanding of layered lake design (bronze silver gold).
• Experience with BI tooling - Apache Superset, Looker, Tableau, or equivalent.
• Strong experience integrating third-party APIs - handling inconsistent schemas, rate limits, and unpredictable failure modes at scale.

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