Senior Data Engineer

Overview

Senior Data Engineer on the Data Enablement team, owning the lakehouse, the pipelines that fill it, and the layers that serve data to people and AI agents.

What you'll do

• Develop and maintain pipelines extracting data from many sources: RDBMS, Change Data Capture and streaming endpoints.
• Process data ranging from observability metrics to bank accounts and transactions, in a highly secure and compliant manner.
• Transform raw data into business-ready analytical models on a Data Lakehouse.
• Guard data consistency, quality and end to end governance across the full lifecycle.
• Ingest and maintain a large versioned documentation corpus: incremental sync, change tracking, deduplication and freshness.
• Build and maintain MCP tools that let AI agents query documentation, API specs and release history.
• Maintain pipelines as software: GitHub Actions CI/CD, automated tests, observability and environment promotion across Fabric and Databricks.

What you'll need

• A Bachelor's or Master's degree in Computer Science, Data Science or a related field.
• 5+ years of experience in data engineering, preferably Microsoft Fabric, Azure Databricks or Spark.
• Strong experience on another major cloud is fine if you are ready to convert.
• Expertise in Data Lakehouse concepts and Delta Lake.
• Working knowledge of data regulation and compliance, and the judgement to know when to involve Legal.
• Strong technical leadership: drive work end to end, take calls with other value streams and stakeholders, and keep it moving without a project manager.
• Excellent written and verbal communication skills in English.
• Proactive, autonomous and self-sufficient.
• Python and SQL depth, including pipeline and query performance work.

Nice to have

• Experience with data contracts and making a platform other teams can self-serve from.
• A BI layer in production, such as Power BI.
• Machine learning fundamentals, enough to work with AI engineers as a peer.
• Retrieval and vector search: building or maintaining the vector and hybrid search layer that AI products query.
• Preparing data for model training or fine-tuning, including anonymisation of sensitive data.
• Instrumenting retrieval so its quality can be measured by someone else.

Details

• Location: Hyderabad.