Azure Databricks Data Engineer for Federal Systems
Opportunity Summary
This data engineering role focuses on building scalable ingestion, transformation, and integration pipelines to move data from legacy systems into modern cloud data platforms. You will implement and optimize data processing solutions using Azure Databricks and lakehouse architectures to support analytical, operational, and reporting use cases. The position also includes support for phased data migration from legacy databases and ETL tools into Azure Databricks. Ideal candidates are comfortable designing robust workflows, ensuring reliability of pipelines, and collaborating to understand source system constraints for federal environments.
Ideal Candidate
A strong mid level data engineer who has implemented ETL and lakehouse solutions on Azure Databricks and understands how to migrate and validate data reliably. You communicate clearly with stakeholders and take ownership of pipeline quality and observability.
Project Insights
- Emphasize how you modeled data for lakehouse patterns and downstream reporting
- Discuss strategies for incremental loads, validation, and monitoring
- Share examples of performance tuning for large scale transformations
How to Prepare
- Prepare an architecture overview of one ETL to lakehouse migration you led
- Mention monitoring and data quality checks you implemented
- Be ready to discuss tradeoffs between batch vs incremental processing in your projects
Typical Challenges
- Modernizing ETL workflows while minimizing disruption to legacy sources
- Ensuring pipeline reliability and data quality in cloud environments
- Handling migration constraints and differences across legacy schemas
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Project Details
Skills & Requirements
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