Join Optum, a global leader in health services, and leverage technology to improve lives. As a Data Engineering Lead, you will drive the development of scalable data solutions that empower reporting, analytics, and AI initiatives. This role offers a dynamic culture, talented colleagues, and opportunities for career growth, all while advancing health optimization globally. Be part of a team that's Caring, Connecting, and Growing together.

This position is based in Noida, Uttar Pradesh, India.

Lead the design, development, and maintenance of scalable dimensional data models, including Star and Snowflake schemas. Build curated data products supporting analytics, AI, and machine learning. Define and standardize business logic, KPIs, and metrics. Create reusable datasets and semantic models to ensure consistency. Develop modern data architectures using Databricks Lakehouse principles, including Bronze, Silver, and Gold data layers. Implement scalable ETL/ELT pipelines using Spark, PySpark, and SQL. Optimize Delta Lake implementations and CI/CD pipelines. Design and manage enterprise semantic and context layers for a single source of truth. Enable self-service analytics and manage semantic models for reporting consistency. Analyze and optimize query performance, Spark workloads, and cloud expenses. Implement data quality frameworks and ensure compliance with governance and regulatory requirements. Mentor junior engineers and collaborate with cross-functional teams to influence data platform strategy.

A graduate degree or equivalent experience is required, with a Bachelor's degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field. You should have 7+ years of experience in Data Engineering, Analytics Engineering, or Business Intelligence, including 3+ years with Databricks and Delta Lake. Hands-on experience with BI platforms like Power BI or Tableau, building enterprise-scale ETL/ELT pipelines, and implementing semantic layers is essential. Proficiency in performance tuning Spark workloads, cloud platforms (Azure, AWS, or Google Cloud), and DevOps practices with Git is necessary. A strong understanding of Databricks Lakehouse architecture, Medallion design patterns, data governance, metadata management, and data quality frameworks is crucial. Advanced SQL and Spark/PySpark development skills, along with excellent problem-solving, communication, and stakeholder management abilities, are expected.