Data Engineer - Archimedes
Tasks
- Apply secure data engineering practices
- Build API-based integrations
- Build bronze/silver/gold data layers
- Build vectorization pipelines
- Collaborate with cross-functional teams
- Create data quality and metadata frameworks
- Deliver AI ready data products
- Design healthcare data integration patterns
- Design lake house architectures
- Develop canonical enterprise data models
- Develop data integrations with cloud services
- Ensure data governance and compliance
- Establish DataOps practices
- Implement CI/CD workflows
- Implement ETL pipelines
- Implement event-driven architectures
- Maintain documentation
- Model and manage structured and unstructured data
- Monitor and troubleshoot pipeline performance
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | AWS Glue | Artificial Intelligence | Automation | Azure Data | Azure Data Factory | Azure Data Lake | Azure Data Lake Storage | Azure Data Lake Storage Gen2 | Azure Databricks | Azure DevOps | CI/CD | Data Catalog | Data Factory | Data Governance | Data Lake Storage Gen2 | Data Lineage | Data Pipeline Monitoring | Data Quality | Data masking | Data pipeline | DataOps | Delta Lake | ETL | Encryption | Event Driven | Event Grid | Event Hub | Event-driven architecture | EventBridge | GitHub Actions | Infrastructure as Code | Jenkins | Lambda | Machine Learning | Metadata Management | Observability | Pipeline monitoring | PostgreSQL | RAG | RBAC | Retrieval-Augmented Generation | Row Level Security | SQL | SQL Server | Semantic Search | Service Bus | Synapse Analytics | Unity Catalog | Vectorization | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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