Databricks Data Engineer – Build Scalable Data Pipelines

About the role

We are looking for a Databricks Data Engineer to design, build, and operate scalable data pipelines and curated data products on the Databricks platform. You will work across ingestion, transformation, governance, and delivery layers using SQL, Python, and PySpark while applying strong data‑warehousing principles.

Key responsibilities

• Build and maintain production‑grade data pipelines in Databricks using SQL, Python, and PySpark.
• Implement ELT/ETL patterns for batch and streaming data processing.
• Develop Lakehouse data models and curated datasets following Kimball, Inmon, or Data Vault methodologies.
• Use Lakeflow Declarative Pipelines (DLT) and Expectations to ensure robust, maintainable pipelines with built‑in data quality checks.
• Configure Unity Catalog for governance, access control, and lineage.
• Optimize performance and cost through cluster sizing, partitioning, caching, and query tuning.
• Collaborate with analytics, data‑science, and engineering teams to define data contracts and deliverables.
• Create technical documentation and support operational excellence, including incident response and root‑cause analysis.

Required profile

• Proven hands‑on experience with Databricks in production environments.
• Strong knowledge of SQL, Python, and PySpark for data‑engineering workloads.
• Experience with data‑warehousing design (Kimball, Inmon, or Data Vault).
• Understanding of data‑engineering fundamentals such as orchestration, incremental processing, SCDs, metadata management, and observability.

Required skills

• Databricks platform (Lakeflow/DLT, Expectations, Unity Catalog)
• SQL
• Python
• PySpark
• Data‑warehousing methodologies (Kimball, Inmon, Data Vault)
• Microsoft SQL Server / T‑SQL (nice‑to‑have)
• Azure services (ADLS, networking, security, identity)
• Git‑based workflows and CI/CD for data pipelines

What we offer

• Starting salary of €5,500 per month (B2B contract)