Public summary
An international electronics and software company in Munich is seeking a Data Engineer to design and maintain scalable ETL and data ingestion pipelines. The role involves working with Databricks, PySpark, Snowplow, dbt, and various databases, supporting analytics and data quality initiatives. The company offers a hybrid working environment, training budgets, employee benefits, and a collaborative start-up culture.
Location and work setup
- Location
- Munich
- Remote status
- Hybrid
- German requirement signal
- No German Required Detected
- Detected job language
- English
Responsibilities
Design, build, and maintain scalable ETL and data ingestion pipelines using Databricks and PySpark from development through deployment and optimization. Configure and support Snowplow event tracking to ensure high-quality behavioral data. Develop and maintain data transformation and semantic models using SQL and dbt for reliable datasets enabling self-service analytics. Collaborate with software engineers and business stakeholders to understand data requirements, improve data quality, and support integrations across the data platform including various databases and APIs. Monitor pipeline performance, share best practices, mentor colleagues, and explore new technologies to enhance data engineering capabilities.
Qualifications
Bachelor's degree in Computer Science, Information Technology, Data Engineering, or related field. Hands-on experience building ETL and data ingestion pipelines, strong skills in Databricks, SQL, Python, and PySpark. Experience with Snowplow for event tracking and analytics data collection is required; familiarity with dbt is highly advantageous. Exposure to at least one of PostgreSQL, MongoDB, or DuckDB. Excellent communication skills and ability to collaborate and problem-solve effectively. Interest in mentoring and contributing to a data-driven culture is valued.