Enterprise Data Warehouse (EDW) ETL/Data Engineer
Tasks
- Build lakehouse ingestion and transformation workflows
- Create monitoring logging and auditing solutions
- Design and develop scalable data pipelines
- Design dimensional data models
- Develop PySpark notebooks and distributed jobs
- Implement CI/CD for data pipelines
- Implement data vault patterns and slowly changing dimensions
- Implement medallion architecture solutions
- Lead cloud data modernization and migration initiatives
- Optimize distributed SQL workloads and query performance
- Perform workload assessment capacity planning and cost optimization
- Support code reviews and release management
- Troubleshoot and respond to production incidents
Perks/Benefits
- N/A
Skills/Tech-stack
ARM Templates | Active Directory | Azure Active Directory | Azure Data | Azure Data Factory | Azure Data Lake | Azure Data Lake Storage | Azure Data Lake Storage Gen2 | Azure Databricks | Azure DevOps | Azure Key Vault | Azure Monitor | Azure SQL | Azure Synapse | Azure Synapse Analytics | Bash | Bicep | CD pipelines | CI/CD | CI/CD pipelines | Change Data Capture | Data Capture | Data Factory | Data Lake Storage Gen2 | Data Vault | Databricks Delta | Databricks Delta Lake | Dedicated SQL Pool | Delta Lake | Dimensional Modeling | Entra ID | Execution Plan | Execution Plan Analysis | Fabric Lakehouse | Indexing | Integration runtime | KQL | Key Vault | Kimball Method | Log Analytics | Managed Identity | Microsoft Fabric | Pandas | Parquet | Partitioning | Plan analysis | Powershell | PySpark | Pytest | Query Optimization | REST API | SQL | SQL Pool | SQL Pools | Serverless SQL | Serverless SQL Pools | Service Principal | Slowly Changing Dimensions | Spark Pools | Synapse Analytics | Synapse Dedicated SQL Pool | T-SQL | Teradata | Terraform | Unit Testing
Education
N/A
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