Data Engineer
First seen by Alion on Oct 6, 2026.
Total Experience : 3 - 7 years
We are looking for 3+ years of experienced Data Engineers.
Responsibilities :
- Actively participate in the design, implementation, and continuous improvement of end-to-end data platform architecture using modern Azure cloud technologies including Azure Data Factory, Azure Data Lake Storage, and Microsoft Fabric (Lakehouses and Warehouses).
- Build scalable, reliable ELT/ETL pipelines to ingest, process, and transform data from multiple source systems including GCAS, GSS, Insurance, Savvy, and BAS systems.
- Implement and maintain automated data quality checks and pipeline testing to ensure reliability and trust.
- Develop and maintain data lakehouse solutions implementing medallion architecture patterns with bronze, silver, and gold layers for progressive data refinement.
- Collaborate with offshore data engineering partners to deliver data engineering initiatives, providing technical guidance and ensuring quality standards.
- Assist the Business Intelligence team with reporting and analytics requirements, ensuring their needs are supported through effective data solutions.
- Contribute to and adhere to established data governance and security practices, including metadata management, data quality frameworks, and data cataloging, to promote trust and reliability.
- Enable business functions with clean, structured data that supports compliance reporting, customer insights, fraud detection, and product development.
- Provide detailed documentation, knowledge transfer, and training to internal teams to build data literacy.
Tech Stack:
- Microsoft Fabric (Lakehouse, Warehouse, Dataflows)
- Azure Data Factory (ADF)
- Azure Data Lake Storage (ADLS Gen2)
- Python
- SQL (Advanced queries, performance tuning, data transformation)
- ETL/ELT Pipeline Development
- Data Modeling & Schema Design
- Data Quality and Validation Frameworks
- Git Version Control
- CI/CD Fundamentals
Skills
Data Engineering, Azure, Microsoft Fabric, Cloud, Azure Data Factory, ETL, Data Ingestion, Data Storage, Data Modeling, Data Quality