Hire data engineers in India, powered by Mira AI.

Open the role around the movement of data rather than the tools: where it comes from, where it has to land, how fresh it needs to be, and who breaks if it stops. Mira AI drafts the job description and a scorecard built on that, the role reaches our candidate community, and each application is scored as it arrives. You interview in Mira AI's order and Wisemonk employs whoever you pick.

A data engineer moves data and keeps it moving: ingestion, orchestration, streaming, storage and the infrastructure beneath. An analytics engineer works inside the warehouse once the data has landed, turning raw tables into models the business can query with confidence, usually in dbt and SQL. Teams with messy sources need the engineer first. Teams whose dashboards disagree with each other usually need the analytics engineer.

Data engineering prices close to strong back-end engineering and above analytics roles. Streaming and platform specialists sit at the top, warehouse and SQL-focused engineers at the more affordable end, and Databricks or Spark experience carries a premium because those pools are smaller than the CVs imply. When you open a role we check your band against our payroll data and say whether it will fill before the role goes live.

A back-end engineer builds the systems that create data; a data engineer builds the systems that collect, reshape and serve it for analysis. The two overlap in SQL and Python and diverge almost everywhere else, particularly around modelling, backfills and orchestration. If your problem is that the product needs a new service, that is back-end work. If the problem is that nobody can answer a question about the product, that is data engineering.

Name the platform in the brief, because the day-to-day work genuinely differs, but treat it as a preference rather than a hard filter below senior level. A strong SQL and Python engineer moves between warehouses in a few weeks. Where the platform really matters is Spark and Databricks, since distributed processing is a skill in itself and not something picked up over a weekend.

Overlap with the UK and Europe is easy to arrange. A full US shift narrows the field somewhat, though data engineering tolerates offset hours better than most roles, because the pipelines run overnight anyway and much of the work is asynchronous. Where overlap matters is incident response, so say plainly whether the role carries any on-call expectation.

Ask about a failure. Anyone who has genuinely operated a pipeline has a story about a silent data quality problem, a backfill that went wrong, or a bill that arrived larger than expected, and they can tell you what they changed afterwards. Mira AI weighs systems still running above migrations and proofs of concept, and explains each ranking so you can see who has operated rather than only built.

No. Wisemonk signs as the legal employer and carries payroll, provident fund, gratuity, ESI and income tax withholding on your behalf. The engineer takes direction from your team throughout. If your company already runs an Indian entity, the hire can sit on it instead, and you decide that at the offer rather than upfront.

Around six to nine years, owning the model and the platform rather than a set of jobs: how the warehouse is structured, which orchestration patterns the team follows, what gets tested, and what the whole thing costs to run each month. Service-company titles move quickly here, so put the ownership you need into the scorecard instead of trusting the label.

Managed ingestion tools are genuinely good and worth using for standard sources. They stop helping when your sources are unusual, when the volume makes per-row pricing painful, or when the real work has moved from getting data in to modelling it properly once it is there. Most teams should start with the tool and hire the engineer when the bill or the modelling backlog makes the case for itself.