Senior Data Engineering Lead
Optum (company site, opens in a new tab)8+ yrsGurugramOn-siteDirect career pageEst. 30–48 LPA
Posted 16h ago
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Job description
Structured overview of role & requirements
About This Role
- Design and optimize scalable batch, streaming, and near-real-time data pipelines for cloud and enterprise platforms with focus on data quality and governance.
- Develop AI-ready data products including semantic and knowledge layers, implementing GenAI data patterns like embeddings, vector search, and RAG pipelines.
- Build backend services, APIs, and microservices, enabling integration of structured and unstructured data for analytics, ML, GenAI, and enterprise workflows.
Minimum Requirements
- Graduate degree or equivalent experience.
- 8+ years of hands-on experience in data engineering, data platforms, backend engineering, or related roles.
- Proven experience with Databricks, Snowflake, BigQuery, Azure Synapse, or similar modern data platforms, and cloud platforms such as Azure, AWS, or GCP.
- Hands-on experience developing REST APIs, microservices, backend services using Python, and practical exposure to GenAI data enablement (e.g., RAG, embeddings, semantic search).
Ideal Candidate Profile
- Experienced in building AI/ML-focused data products with strong knowledge of data quality, metadata management, cataloging, and governance in regulated enterprise environments.
- Skilled in modern data engineering stacks including Spark, PySpark, Kafka, and cloud-native data services, comfortable with semantic layers and knowledge graphs.
- Able to collaborate cross-functionally with AI/ML engineers, platform teams, and business stakeholders to translate data needs into scalable, secure, production-ready solutions.