Product Engineering (Fabric Data Engineering)
Tasks
- Apply dimensional modeling using star schemas
- Build OneLake lakehouse warehouse ingestion patterns
- Build Power BI Direct Lake semantic models
- Build silver layer data transformations
- Configure OneLake security model
- Create bronze layer ingestion pipelines
- Design Microsoft Fabric workspace architecture
- Develop CI CD release processes
- Develop DAX measures
- Enable AI and Copilot readiness for analytics
- Expose data via Fabric API for application consumption
- Implement SCD Type 2 history tracking
- Implement automated data quality controls
- Implement gold layer dimensional models
- Lead root cause analysis and coordinate remediation
- Maintain documentation and runbooks
- Mentor engineers and review pull requests
- Optimize Delta tables and Spark workloads
- Set up observability and monitoring
- Troubleshoot production data pipelines and reports
Perks/Benefits
- Community volunteering
- Employee discounts
- Flexible work arrangements
- Good Office Program
- Personal days
- Retirement savings matching
- Student loan payment matching
- Tuition reimbursement
- Wellness allowance
Skills/Tech-stack
Azure DevOps | Azure Monitor | BI Semantic Model | CI/CD | Change Data Feed | Column-Level Security | DAX | DLP | Data Governance | Data Quality | Data feed | Delta Lake | Direct Lake | ELT | Git | GitHub Copilot | GraphQL | Kimball methodology | Lakehouse | Liquid clustering | Log Analytics | Microsoft Fabric | Microsoft Purview | Object-Level Security | OneLake | Optimize | Power BI | Power BI Semantic Model | PySpark | Python | REST | Row Level Security | SQL | Semantic Model | Slowly Changing Dimension | Slowly Changing Dimension Type 2 | Spark | Star Schema | Type 2) | V-Order | VACUUM | Warehouse | Z-Order
Education
N/A
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