Data Engineer

Job Family: IT Architecture/Cloud (India) Travel Required: Up to 10% Clearance Required: None The Data Engineer will build, maintain, and support data pipelines and transformations that enable reliable analytics, reporting, and AI solutions across the enterprise data platform. The role will focus on data ingestion, Bronze/Silver/Gold transformations, data quality, deployment, production operations, assigned semantic models, and technical documentation. This is a hands-on data engineering role. The Data Engineer will deliver solutions using approved frameworks, architecture patterns, and standards established by the Principal Data Platform Architect, partnering with platform, analytics, security, and business teams. Key Responsibilities Build and maintain source ingestion pipelines using approved integration patterns for enterprise applications, databases, APIs, and files. Develop and maintain Bronze/Silver/Gold transformations using approved frameworks and reusable engineering practices. Implement validation and data-quality controls, including completeness, accuracy, consistency, and reconciliation checks. Follow GitHub-based CI/CD, testing, release management, and environment promotion processes. Monitor production pipelines, investigate failures, troubleshoot issues, and implement corrective actions. Support assigned semantic models, including data preparation, refresh dependencies, and troubleshooting in partnership with Analytics. Maintain technical documentation, source-to-target mappings, transformation logic, lineage, operating procedures, and job aids. Follow established security, access, naming, and development standards. Partner with the Principal Data Platform Architect to ensure engineering deliverables align with approved architecture and platform guardrails. Collaborate with business and analytics stakeholders to clarify data requirements and deliver reliable, reusable datasets. Required Experience Hands-on experience building and supporting enterprise data ingestion pipelines and transformations. Strong SQL skills and experience with Python, PySpark, or comparable data engineering tools. Experience working with Azure-based data platforms, Databricks, Microsoft Fabric, or comparable technologies. Experience implementing lakehouse architecture and Bronze/Silver/Gold transformation patterns. Experience integrating data from databases, APIs, files, and enterprise applications. Experience implementing data validation, data-quality controls, and reconciliation processes. Experience using GitHub, version control, CI/CD, testing, and environment promotion processes. Experience monitoring and troubleshooting production pipelines, including failed jobs, refresh issues, and data discrepancies. Ability to support assigned semantic models and their upstream data dependencies. Ability to maintain clear technical documentation and follow established security, governance, and development standards.