Senior Data Engineer(m/w/x)

Requirements

• Bachelor’s Degree in computer science or related STEM field
• 5+ years hands-on Data Engineering experience
• Strong Python and SQL proficiency
• Solid Batch and stream processing experience
• Proven Dimensional Data Modelling and Data Vault experience
• Data Orchestration tools experience (Airflow or Dagster)
• Familiarity with data quality and validation frameworks
• Metadata tools integration experience (Collibra, OpenMetadata)
• Strong Git and CI/CD pipelines understanding
• Cloud platforms experience (AWS preferred)
• Practical Data Lakehouse experience (Databricks, Snowflake)
• Proactive mindset, strong ownership, initiative, drive
• Strong communication skills, professional English proficiency
• BPM software workflow configuration experience (Camunda)
• ML teams experience, ML/DL/NLP concepts familiarity
• Valid work permit

Tasks

• Architect, build, and scale modern data platforms
• Lead design and delivery of enterprise data infrastructure
• Implement Databricks lakehouse solutions
• Apply Delta Lake and Unity Catalog principles
• Utilize Medallion architecture (Bronze/Silver/Gold)
• Design, build, and maintain scalable ELT pipelines
• Leverage Databricks workflows and Delta Live Tables
• Develop pipelines with Apache Spark
• Develop and optimize high-throughput streaming and batch pipelines
• Use Spark Structured Streaming and Auto Loader
• Tune Databricks data platform performance
• Optimize Databricks data platform costs
• Govern Databricks cluster and compute resources
• Define and enforce data contracts and schemas
• Enforce data governance standards
• Utilize Unity Catalog and Delta Lake for governance
• Ensure data quality, observability, and lineage
• Use Databricks Data Observability tools
• Utilize Great Expectations for data validation
• Collaborate with data scientists, analysts, and platform teams
• Deliver reliable, self-serve data products
• Establish and champion data engineering best practices
• Establish and champion data engineering standards
• Establish reusable data engineering frameworks
• Stay current with Databricks ecosystem developments
• Monitor lakehouse architecture trends
• Track emerging data engineering patterns
• Participate in code reviews
• Maintain high standards for code quality
• Ensure code performance and security
• Engage in Agile/Scrum ceremonies
• Contribute architectural insights
• Provide technical direction to the team

Work Experience

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