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