We’re looking for a Data Engineer to help build and run the data platform that powers analytics, operational reporting, and AI-enabled products.

This is fundamentally a data engineering and systems-thinking role. We care less about fluency in a particular framework and more about your ability to reason across the full data lifecycle—from ingestion and raw storage through transformation, modelling, serving, and production operations. You should understand how decisions at one stage of a pipeline affect correctness, reliability, performance, cost, and usability downstream.

What you’ll work on

- Build and operate production data pipelines that remain reliable under high-frequency and high-volume workloads—not just in development or notebooks.
- Design lakehouse and warehouse architectures, data models, and Change Data Capture (CDC) pipelines that keep operational and analytical systems in sync without compromising correctness.
- Develop ingestion frameworks for structured and unstructured data, with careful consideration for schema evolution, data quality, storage formats, partitioning, and downstream usability.
- Design the movement of data across ingestion, raw, transformation, curated, and serving layers, making deliberate trade-offs around latency, scalability, maintainability, and cost.
- Build the data foundations that support AI and GenAI workloads. This includes preparing reliable, usable structured and unstructured data—and recognising when a simpler, non-AI solution is more appropriate.
- Partner with analytics, product, and engineering teams to turn data into trusted monitoring, operational intelligence, and decision-making capabilities.
- Improve platform reliability through monitoring, alerting, failure recovery, backfills, performance tuning, and systematic debugging.

What we’re looking for

- 2+ years of experience in data engineering or platform engineering,



including ownership of systems that have operated under real production workloads.
- Solid Python and SQL skills, along with practical experience building maintainable data pipelines and processing structured or unstructured data.
- End-to-end data engineering judgment. You can reason through how data is ingested, represented in the raw layer, transformed, modelled, stored, and served—and explain the trade-offs at each stage.
- Experience with analytical data modelling and an understanding of operational data models. You know when to normalize or denormalize, how to model around real query patterns, and how designs behave as data volume and traffic grow.
- An understanding of lakehouse or warehouse architecture, CDC, schema evolution, data quality, and the correctness challenges involved in keeping systems synchronized.
- Production ownership across the data lifecycle: ingestion, transformation, storage, modelling, and serving, as well as monitoring, alerting, debugging, backfills, and incident recovery.
- The ability to identify bottlenecks and failure modes, reason about what will break first under load, and make architectural decisions based on evidence rather than convention.
- Evidence of how you approach engineering problems—not just which tools you have used. This could be a system you built, operated, debugged, scaled, or redesigned and the reasoning behind your decisions.

Critical Skills

We value depth in data engineering over familiarity with a long list of tools. That depth includes understanding the entire path data takes through a system. We want someone who can explain where correctness can be lost, where bottlenecks emerge, why a particular storage or modelling approach fits the problem, and how the system will behave as scale and requirements change.

If you can walk through a data platform end to end—explain its design choices, identify its risks, and describe how you would keep it reliable in production—we’d like to talk to you.

📌 Data Engineer (Mumbai)
🏢 IndiaDataHub
📍 Mumbai