Senior Data Engineer

Key Responsibilities

• Design, build, and maintain production grade ETL/ELT pipelines using SQL, Python, PySpark, and Spark SQL.
• Own end to end data pipelines from ingestion through curated, analytics ready datasets.
• Maintain and enhance existing integrations to ensure stability, performance, and scalability.
• Troubleshoot complex pipeline failures and act as a key escalation point for production support.

• Collaborate with Lead Engineers and Data Architects on on-prem to cloud migration initiatives.
• Assess legacy pipelines and integrations, contributing to migration sequencing, refactoring, and risk mitigation.
• Ensure continuity of business-critical data flows during phased migrations and platform transitions.

• Build and operate solutions across hybrid data platforms, including SQL Server, Azure Synapse, and Microsoft Fabric.
• Develop and maintain Fabric Lakehouses, Warehouses, Pipelines, and Notebooks aligned with architectural standards.
• Optimize workloads for performance, reliability, and cost efficiency.

• Implement data validation, reconciliation, monitoring, logging, and alerting to ensure trusted data products.
• Apply disciplined engineering practices: version control, CI/CD, testing, and documentation.
• Improve the operational maturity and production readiness of data solutions.

• Mentor junior data engineers through code reviews, guidance, and best practice sharing.
• Promote consistent engineering standards and support team onboarding.
• Collaborate closely with BI teams, data owners, and governance stakeholders to ensure alignment with analytics needs.

• Identify and drive improvements in pipeline efficiency, migration effectiveness, and developer productivity.

Required Qualifications

• 7+ years of experience in Data Engineering, Analytics Engineering, or a related technical role.
• Strong hands‑on experience with SQL, Python, PySpark, and Spark SQL in distributed data environments.
• Proven experience building and supporting enterprise‑scale ETL/ELT pipelines in the cloud.
• Strong experience with Azure‑based data platforms, including Azure Synapse Analytics and/or Microsoft Fabric.
• Solid understanding of cloud‑native data architectures, analytics engineering patterns, and medallion architectures.
• Experience with Git‑based source control, CI/CD pipelines, and automated testing for data platforms.
• Strong problem‑solving skills and experience supporting mission‑critical production data systems.
• Proven ability to collaborate across engineering and business teams and mentor junior engineers.

Preferred / Nice to Have

• Hands‑on experience with Microsoft Fabric Lakehouse and Spark workloads
• Experience with Azure Data Factory, Fabric Pipelines, or orchestration tools
• Familiarity with data governance, lineage, and certification in cloud platforms
• Relevant certifications such as: Microsoft Certified: Azure Data Engineer Associate, Microsoft Fabric Analytics Engineer

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