Lead Engineer, Data Platform

Overview

Lead Engineer, Data Platform is a hands-on individual contributor role that leads platform initiatives within a technical direction set by the Data Infrastructure team, taking them from design through to production.

What you'll do

• Design and deliver self-service paths so engineers can onboard new sources, datasets and pipelines without a ticket queue, with access, masking, quality and cost controls defined in infrastructure as code and CI/CD.
• Improve ingestion and transformation capabilities so data is fresher and more reliable, handling ordering, duplicates, late data, deletes, replay and backfill, and migrate pipelines without breaking downstream tables.
• Apply governance in what you build: residency, PII masking, least-privilege and just-in-time access, retention and erasure, and lineage.
• Set SLOs, freshness and quality checks, and alerting that tells on-call what matters.
• Find the main cost drivers in data platforms and pipelines, and add guardrails and attribution that teams can act on.
• Break work into phases that ship, with clear milestones, a migration path and a way to measure adoption.
• Use AI-assisted development to build and run the platform faster, and help prepare governed, well-described data that agents can use safely.
• Treat internal engineers as customers: write clear documentation and golden paths, and act on their feedback.
• Review designs and code for other engineers, and raise the bar for production-grade data and API work.

What you'll need

• Independently designed, shipped and operated a meaningful platform or data infrastructure initiative used by internal engineering teams.
• Solid data engineering fundamentals: batch and streaming ingestion, CDC and event-driven patterns, data modelling, and correctness (idempotency, ordering, late data, backfill).
• Strong production engineering in Python and SQL, including building and operating APIs, pipelines or shared libraries.
• Practical infrastructure as code (Terraform or equivalent) and CI/CD for data and infrastructure, including IAM across environments.
• Working knowledge of data governance in practice: PII masking, access control, residency, retention and lineage.
• Observability for data pipelines: freshness and quality checks, SLOs, alert thresholds and incident response.
• The ability to justify technology choices against requirements, with an order-of-magnitude view of cost and capacity.
• Hands-on use of AI-assisted development with sound engineering judgement.
• Clear written and verbal communication, and the ability to explain trade-offs to engineers and non-engineers across teams and time zones.

Nice to have

• Experience enabling AI tooling for engineering teams.
• Experience with global, decentralised teams, and in financial services or other regulated environments.
• Comparable cloud and data tooling experience is welcome; you do not need to match every tool in the stack.

Details

• Location: India, Delhi NCR.