Senior Data Engineer

Our Client engineers technologies that enable infrastructure, food supply, energy efficiency, and climate-friendly solutions. They serve customers worldwide with products used in refrigeration, air conditioning, heating, motor control, mobile machinery, renewable energy, and district heating infrastructure. The company is a world leader since 1933, privately held by the founding family, employing over 27,000 employees and serving more than 100 countries. Jit Team is a Polish IT company with over 770 experts delivering innovative IT solutions and supporting educational and charitable initiatives.

• Minimum 4 years of commercial experience building and operating production data pipelines in Python.
• Strong practical knowledge of SQL.
• Hands-on experience with workflow orchestration tools like Airflow, Dagster, or Mage.
• Experience with stream processing technologies such as Kafka, Faust, Flink, NATS, or similar.
• Experience working with time-series data and production databases such as TimescaleDB, GreptimeDB, or InfluxDB.
• Understanding of time-series data challenges including missing data and sensor drift.
• Experience implementing data quality checks.
• Hands-on experience with Docker, Git, and CI/CD.
• Professional English proficiency at minimum B2 level.
• Ability to work independently and collaborate within a cross-functional team.

Nice to have:

• Experience with stateful stream processing in Python on Kafka (Faust or equivalent).
• Familiarity with Azure, medallion layering, Redis, and Kubernetes.
• Experience ingesting weather or electricity market data.
• Background in Energy, Utilities, Industrial IoT, or OT/SCADA.
• Experience working with data scientists, understanding train/serve consistency and reproducibility.

• Develop and operate production data pipelines using Python.
• Design and maintain workflow orchestration with tools such as Apache Airflow, Dagster, or Mage, handling backfills, retries, and dependency management.
• Implement stream processing solutions with technologies like Apache Kafka, Faust, Flink, or similar.
• Work with time-series data and manage production time-series databases such as TimescaleDB, GreptimeDB, or InfluxDB.
• Address common time-series data challenges including missing data and sensor drift.
• Implement data quality checks as part of data pipelines.
• Use Docker, Git, and CI/CD for development and deployment.

Nice to have:

• Experience with Azure or Google Cloud Platform.
• Knowledge of Redis and Kubernetes.
• Experience ingesting weather forecast or electricity market data.
• Background in Energy, Utilities, Industrial IoT, or OT/SCADA.
• Experience building data platforms for Data Science teams ensuring train/serve consistency and reproducibility.