Data Engineer for AI (f/m/d)
Tasks
- Build GenAI RAG data pipelines
- Build batch pipelines
- Build embedding pipelines
- Build streaming pipelines
- Define data SLAs and SLOs
- Enable self service documentation
- Encrypt data at rest and in transit
- Enforce Unity Catalog governance
- Implement data quality checks
- Implement ingestion patterns
- Implement transformation frameworks
- Ingest data from external sources
- Ingest data from internal sources
- Integrate Databricks with Azure services
- Maintain feature store versioning
- Manage access control and auditing
- Manage feature engineering pipelines
- Onboard new data sources
- Optimize Spark workloads
- Optimize data storage layouts
- Perform root cause analysis
- Provide L3 production support
- Provision Lakeflow connectors
- Publish trusted data products
- Refresh vector indexes
- Set up data monitoring and observability
- Set up governance controls
- Transform and validate data
Perks/Benefits
- Childcare facilities
- Company share purchase plan
- Flexible working hours
- Hybrid work model
- Job bike leasing
- Jobticket subsidy
- Mental health and wellbeing programs
- Pension savings plan
- Relocation support
- Self guided learning and development
- Volunteering time
Skills/Tech-stack
Access Control | Azure | CI/CD | Data Contracts | Data Governance | Data Modeling | Data Quality | Databricks | Delta | Delta Lake | Embeddings | Encryption | Feature Engineering | Feature Store | Infrastructure as Code | Lakehouse | Lineage | Medallion Architecture | Microsoft Foundry | Monitoring and observability | Orchestration | Python | RAG | SQL | Spark | Terraform | Unity Catalog | Vector indexing | “as-code”
Roles
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