Data/Databricks Engineer v Czech Republic, Praha

We're looking for a  Middle+ Data/Databricks Engineer to join our data team and take ownership of pipelines built on Databricks. You'll design and run production-grade ETL/ELT workloads, work with the lakehouse (Delta Lake) architecture, and partner closely with analysts, data scientists, and business stakeholders who rely on the data you deliver. "Middle+" here means roughly 3–5 years of hands-on data engineering experience: you can own a pipeline end to end with minimal supervision, make sound design decisions, and are starting to mentor more junior engineers — without yet carrying full architectural ownership of the platform.

Contract & Logistics

• Contract type: B2B (valid Czech IČO / trade license required).
• Location: Hybrid schedule with 1-3 times visit to Prague office.
• Start date: ASAP
• English: Fluent 
• Czech: Native/Fluent 

What You'll Do

• Design, build, and maintain scalable ETL/ELT pipelines on Databricks using PySpark and SQL.
• Implement and evolve Delta Lake tables following a medallion (bronze/silver/gold) architecture.
• Develop batch and, where needed, streaming data processing jobs (Structured Streaming).
• Build and maintain CI/CD for Databricks notebooks and jobs (Databricks Asset Bundles, Repos, Git-based workflows).
• Orchestrate pipelines with Databricks Workflows, Azure Data Factory, Airflow, or similar tools.
• Monitor job performance and cluster costs; troubleshoot failures and optimize for reliability and spend.
• Implement data quality checks, testing, and monitoring for critical datasets.
• Collaborate with analysts, data scientists, and business stakeholders to translate requirements into data models.
• Participate in code reviews, document technical decisions, and help shape engineering best practices.
• Provide guidance and informal mentorship to junior team members.

What We're Looking For

• 5+ years of hands-on experience with Apache Spark, ideally on Databricks specifically.
• Strong PySpark and SQL skills, including performance tuning of Spark jobs.
• Practical experience with Delta Lake and lakehouse architecture concepts.
• Solid grasp of data modeling (dimensional modeling, star schema) and ETL/ELT design patterns.
• Experience with at least one major cloud platform — Azure, AWS, or GCP (Azure Databricks is a plus).
• Familiarity with pipeline orchestration tools (Databricks Workflows, ADF, Airflow, or equivalent).
• Working knowledge of Git and CI/CD practices for data engineering.
• Understanding of data governance, access control, and security basics (Unity Catalog is a plus).
• Good written and spoken English; Czech or Slovak is a plus but not required.
• Comfortable working independently and communicating clearly in a remote, distributed team.

Nice to Have

• Databricks certification (Data Engineer Associate or Professional).
• Experience with streaming technologies such as Kafka or Azure Event Hubs.
• Experience with dbt for transformation and testing.
• Solid Python software-engineering practices — testing, packaging, typing.
• Infrastructure-as-code experience (Terraform).
• Exposure to MLOps or ML pipeline support.
• Experience in a regulated or large enterprise data environment.