Data Engineer (Snowflake Azure) US Shift

Looking for 5+ years of Data Engineer to design, develop, and manage the data platform solution.

Work Time: 9:30 pm - 6:30 am (US Shift)

Mode: Remote

Primary Skills

• Strong experience with Snowflake as a cloud data platform; exposure to GCP is an advantage.
• Hands-on expertise in dbt for data transformation and modelling.
• Experience with Azure Data Factory (ADF) for data pipeline development and orchestration.
• Proficiency in Looker for BI, reporting, and data visualization.
• Familiarity with AI/ML platforms and proprietary AI tools is desirable.
• 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

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