Data Engineer
Tasks
- Build manage and optimize ETL ELT pipelines
- Build manage and optimize SQL based data models and database objects
- Contribute to data engineering documentation and best practices
- Design build and maintain data integration solutions
- Design develop and maintain datasets and semantic models
- Develop data transformation logic
- Implement reusable pipeline patterns and data quality checks
- Ingest transform validate and deliver data
- Modernize and migrate legacy data integration processes
- Research evaluate and recommend emerging data technologies
- Translate business requirements into data solutions
- Troubleshoot data pipeline and data quality issues
Perks/Benefits
- Collaborative work environment
- Community outreach volunteering
- Employee resource groups
- Professional mentoring program
- Training and development
- Work from home
Skills/Tech-stack
API Integration | Alerting | Azure Data | Azure Data Factory | Azure Data Lake | Azure Data Lake Storage | Azure Databricks | Azure Key Vault | Azure Storage | Azure Storage Accounts | Data Factory | Data Ingestion | Data Lake Storage | Data Quality | Data Reconciliation | Data Validation | Databricks Notebooks | Dataflows | ELT | ETL | Indexing | Key Vault | Lakehouse | Logging | Microsoft Fabric | Monitoring | Notebooks | Pipelines | Power BI | PySpark | Python | SQL | SQL Server | Semantic models | Spark SQL | Storage Accounts | Stored Procedures | T-SQL | Views | Warehouse | Web Services
Education
Roles
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