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Hire Data Engineers
Data engineering is where job titles have drifted furthest from the work. Candidates arrive called data engineers who have written SQL against a warehouse somebody else built, or built dashboards, or trained models. The actual job — moving data reliably at a schedule, modelling it so analysts can trust it, and knowing within minutes when a pipeline has silently produced wrong numbers — is done by a much smaller group.
How We Fill It
We screen for pipeline ownership. Which sources, what volume, what orchestration, what happens when an upstream schema changes at 2 am, how data quality is tested, and how the warehouse is modelled. SQL ability is table stakes and is still where a surprising number of candidates fall over, so we test it directly rather than taking it on trust.
The stack splits roughly into modern cloud warehouse work (Snowflake, BigQuery, Redshift with dbt and Airflow), big-data work (Spark, Hive, Databricks), and streaming (Kafka, Flink). These pools overlap less than employers expect, and a batch engineer moving to streaming is a real learning curve, not a weekend.
Timelines & Market
Salary Benchmark
Indicative metro ranges for 2026 — Delhi NCR, Mumbai, Bengaluru, Hyderabad, Pune and Chennai. Tier-2 cities typically run 20–30% lower for the same scope.
| Experience | Indicative CTC | What that buys |
|---|---|---|
| 0–2 years | ₹5–9 LPA | Often analysts transitioning in; screen SQL hard |
| 3–5 years | ₹12–24 LPA | The most competitive band in data today |
| 6–9 years | ₹24–42 LPA | Lead and platform ownership, including cost |
| 10+ years | ₹42–70 LPA+ | Data architects and heads of data platform |
These are negotiating frames drawn from the mandates we run, not a quote. Bands move with sector, funding stage, city and how scarce the specific skill is in that market — ask us for a benchmark against your exact role and location.
Our Screen
Strong SQL including window functions and query tuning, Python for data work, one orchestration tool owned in production, dimensional or layered warehouse modelling, and a pipeline they can describe end to end including its failure modes
dbt, Spark or Databricks, one cloud warehouse at depth, Kafka or another streaming source, infrastructure-as-code exposure, and data quality tooling such as Great Expectations or dbt tests
Whether they built the pipeline or consumed it, what they do about late and duplicate data, how they handle a backfill, and whether cost has ever been their responsibility
JD Outline
Use this as the starting point for your job description — it is the scope we brief candidates on.
Interview Structure
Four rounds, each testing something different. Rounds that repeat each other cost you candidates without improving the decision.
Pipeline ownership, volumes and sources handled, orchestration tooling, warehouse modelling approach, and whether the title matches the work.
Real queries against a messy schema, plus a modelling question. This filters more candidates than any other round, which is exactly why it goes early.
Design ingestion and modelling for a source of yours, with scheduling, late data, idempotency and cost all addressed.
How they work with analysts and product, how they handle a request for a number they do not trust, and how they communicate a broken pipeline.
Why It Stays Open
We raise these at the briefing rather than after a month of silence.
Candidates who have written SQL and built dashboards apply in large numbers for data engineering roles. They are often good hires for an analyst seat and the wrong hire here. The SQL-plus-pipeline-design pair of rounds catches it in a day.
Exactly-once semantics, watermarks and state management in a streaming system are a genuinely different discipline. If your requirement is streaming, say so at briefing and expect a smaller pool and a higher band.
If nobody owns the tests, the warehouse quietly stops being trusted and the role becomes firefighting. Candidates ask about this, and a vague answer loses you good people at the offer stage.
A JD that reads 'Airflow, dbt, Snowflake, Spark, Kafka, Flink' selects for CV keyword coverage rather than engineering ability. We will ask at briefing which two tools are actually in your stack today.
Sector Context
FAQs
With two rounds, in order. A SQL and modelling test comes first, because it removes the largest group of mismatches quickly, then a pipeline design conversation that requires them to handle scheduling, late data, idempotency, backfills and cost. Analysts can usually pass the first and not the second, and that is exactly the line you are hiring across.
Yes, and the warehouse match matters more than most stack matches because the cost and performance models differ. A strong Snowflake engineer is productive on BigQuery within a few weeks, but their instincts about partitioning, clustering and warehouse sizing have to be relearned, so we source warehouse-first where your platform is already decided.
Yes, but it is a headhunt rather than a search. Genuine Kafka or Flink production experience with exactly-once semantics and state management sits with a small group, concentrated in Bengaluru, Hyderabad and Pune. Expect four to six weeks, a shortlist of three to four, and a band at the top of the range.
At the same experience level, typically 10–20% more in 2026, because demand has outrun supply faster on the data side. If your pipelines are simple and your volumes modest, a strong backend engineer with good SQL is often the better value hire, and we will say so rather than selling you the scarcer profile.
Send the requisition to info@recruitmentconsultant.co.in and we will come back with a realistic band and timeline before any search starts.
Send us the job description and get a screened shortlist within 48 working hours. No obligation, no upfront fee, and a 90-day replacement guarantee on every permanent placement.
