Sr. Data Engineer

As a Sr. Data Engineer, you will own the design and delivery of complex data engineering solutions that power enterprise analytics, AI, and reporting capabilities. Reporting to the Lead Data Engineer, you will drive technical decisions, set engineering standards, and ensure the reliability and scalability of data platform across 40+ integrated enterprise source systems.

This role demands deep technical mastery in SQL, Python, and Azure cloud data engineering, combined with a product orientation—understanding how the data assets you build translate into decisions, reports, and AI outputs for the business. You will mentor Data Engineers, contribute to architectural direction, and serve as a technical anchor for delivery across the team's sprint cycles.

Education:

• Bachelor's degree in computer science, Data Science, Information Technology, or a related quantitative field.

Experience:

• 6+ years of progressive experience in data engineering with demonstrated ownership of complex, production-grade data platforms.
• Expert-level SQL (query optimization, indexing strategy, execution plans) and Python (PySpark, pipeline frameworks, testing).
• Deep hands-on experience with Azure data services: Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage.
• Proven experience designing dimensional data models and data lake architecture at enterprise scale.
• Experience building data pipelines that directly support machine learning feature engineering and model serving.
• Strong background in data quality engineering—automated validation, SLA enforcement, and lineage tracking.
• Experience with relational databases (SQL Server, Oracle) and migration from legacy to cloud-native platforms.

Certifications & Licenses:
The following are considered favorable:

• Microsoft Certified: Azure Data Engineer Associate
• Databricks Certified Associate Developer for Apache Spark

Knowledge:

• Advanced SQL and Python for enterprise-scale data engineering—optimization, testing, and framework design.
• Azure data platform architecture in depth—ADF, Databricks, Synapse, ADLS, and their integration patterns.
• Modern data platform paradigms—data lake, medallion architecture, data mesh concepts, and consumption layer design.
• Machine learning pipeline requirements—feature engineering, training data preparation, and model data dependencies.
• Data governance frameworks—metadata management, lineage, cataloging, access control, and regulatory compliance (HIPAA).
• Agile engineering practices—sprint delivery, DevOps hygiene, CI/CD for data pipelines, and technical documentation standards.

For a complete and detailed Position Description - Please Apply with Resume