Lead Data Engineer, AWS Data Platform

Hiring immediately. We are looking for someone who can start now or on a short notice period.

Experience: 5+ years in data engineering, 1 to 2 of those leading Type: Full-time Location: Remote (India) Working hours: 5:00 PM to 2:00 AM IST. Hard requirement, not a preference.

Stack: AWS (S3, Glue, Redshift, Athena, Lake Formation, SNS/SQS, RDS Postgres), Apache Airflow, a metadata-driven ingestion framework, SQL, Python, GitHub, VS Code, GitHub Copilot

About the role

We run a contemporary data platform on AWS that feeds analytics and AI/ML teams at an enterprise client. Data moves from a raw landing zone through curated layers into Redshift, driven by a metadata framework rather than hand-written one-off pipelines, and it runs across both non-production and production environments. We want one person accountable for the design, the standards, and the roadmap of that platform. Senior enough to say no to a bad model before it ships, and calm enough to run a backfill without turning it into a war room.





This is an immediate opening on a live project, so we are moving fast on interviews and offers. If you are available on a short notice period, say so in your application.

The evening shift is real and it is the reason this role is hard to fill. Your day overlaps almost entirely with a US-based stakeholder group, and this role talks to them directly rather than through a proxy. If that overlap does not work for you long term, please do not apply. We would rather lose you now than in month three.

What you will own
- The metadata-driven ingestion framework. Extending it, hardening it, and making it the default path for new sources instead of another bespoke pipeline.
- The raw to land to Redshift flow. Layer contracts, partitioning, file formats, compaction, and load patterns that stay reliable as source count grows.
- The Redshift warehouse. Dimensional models, distribution and sort keys, WLM behaviour, and a real answer for cost per

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