How to hire data engineers in India: lakehouse, streaming and data quality skills to test for

To hire data engineers in India in 2026, you are competing for one of the most requested skill sets in the market, so the assessment has to be sharp. Data engineering and BI is a priority hiring area for 54% of India's GCCs (EY, Nov 2025), and SQL or data engineering skills appear in more than half of AI job postings (foundit via Storyboard18, Jan 2026).

The way to hire well is to test the work your platform will actually need: managing lakehouse tables, processing streaming data without losing or double-counting events, and catching data quality problems before they reach a report or a model. A short pipeline exercise and a design discussion about failure modes will tell you more than certifications or a list of tools on a CV.

Why data engineers are hard to hire in India right now

Demand for data skills has held up even in a soft tech job market. Xpheno, a staffing firm, recorded weak active tech openings in the first half of 2026 (about 108,000 in May), yet data engineering still led its list of most-demanded areas, alongside cybersecurity and cloud platform work (Xpheno via Analytics India Magazine, 2026; Deccan Herald, 2026).

Inside GCCs the pull is stronger. Quess, another staffing firm, reports that AI, data and analytics rose from 16.7% of GCC tech hiring demand in April to June 2026 to 19.7% in July to September, while overall GCC tech demand fell 3.5%. It puts the supply gap for these roles at 38 to 42% (Quess via CXOToday, Nov 2026). Like most job-market figures in India, these come from staffing firms and measure postings or demand rather than headcount, so read them as direction, not precise size.

Lakehouse experience is thinner still. Supersourcing, a recruitment firm, estimates about 28,000 Databricks-certified professionals in India as of May 2026 and says fewer than 15% of them have run Unity Catalog in production (Supersourcing, 2026). We could not find an independent report that confirms those numbers. They do match a pattern most hiring managers will recognize: certification is common, production experience is not.

The role is also moving. Quess lists "data engineer to AI data platform engineer" as one of the reskilling paths GCCs use, and says tech skills now stay relevant for less than two years (Quess via Business Today, Jul 2026). An engineer who only knows last year's stack will need to keep learning, so test how candidates reason as well as which tools they know.

Decide which data engineer you need

"Data engineer" covers several different jobs. Writing one job description for all of them is a common reason searches drag on. Settle the profile before you design the test.

A first team of four to six usually needs one strong platform engineer who sets standards, with the rest weighted toward whichever workload dominates. If the data feeds machine learning, the overlap with MLOps is large; see building an MLOps and LLMOps team in India.

Lakehouse skills to test

A lakehouse stores data in open table formats on object storage and adds warehouse-style management on top. Supersourcing names Unity Catalog, Snowflake Horizon, Apache Iceberg and cost optimization as the core skills buyers ask for (Supersourcing, 2026). Your own stack decides which tools matter, but the concepts below carry across platforms.

• Table formats: how Delta Lake or Iceberg handles schema evolution, time travel and concurrent writes, and when the candidate would pick one over the other.
• Physical layout: partitioning choices, small-file compaction, clustering, and how to change a partition scheme on a live table.
• Incremental loads: idempotent MERGE operations, slowly changing dimensions, and reprocessing a day of data without duplicating it.
• Catalog and access: how permissions, lineage and data classification are managed in the catalog, and how column masking works for personal data.
• Cost: reading a query profile, sizing compute, setting auto-suspend, and spotting a job that scans far more data than it needs.

A good exercise: give the candidate a change-data-capture extract with duplicates, out-of-order updates and one schema change, and ask them to build a clean, deduplicated table from it. Then ask how the design changes when the source sends a correction three days late. Strong candidates talk about keys, ordering columns and replay before they mention any tool.

Streaming skills to test

Streaming pipelines tend to fail quietly. Events arrive late, consumers restart, and totals drift by a fraction of a percent until finance notices. The questions that matter are about correctness when something breaks.

• Event time versus processing time, watermarks, and what happens to data that arrives after a window closes.
• Delivery semantics: at-least-once delivery with idempotent sinks, deduplication keys, and when exactly-once claims actually hold.
• Kafka basics: partitions and ordering, consumer groups, retention, and compatibility rules in a schema registry.
• Stateful processing in Spark Structured Streaming, Flink or a managed equivalent: checkpoints, state size, and what a restart does to state.
• Change data capture from operational databases, including initial snapshots, backfills and replays.

One question works well in a live interview. A consumer was down for six hours, and the business needs correct hourly totals by morning: ask the candidate to walk through recovery. Listen for offsets, checkpoint state, idempotent writes and a reconciliation against the source system. A candidate who suggests simply rerunning the job without asking about duplicates has probably not run streaming in production.

Data quality and observability skills to test

Bad data costs more than missing data, because people act on it. Data quality work is the clearest signal of an engineer who has owned a production platform rather than built pipelines for a course.

Test for:

• Checks at the right layer: schema, null, uniqueness, referential and range checks, written in a framework such as dbt tests or Great Expectations.
• Data contracts with upstream producers, and what happens when a producer breaks one.
• Freshness and volume monitoring, anomaly detection on key metrics, and alerts routed to a named owner.
• Reconciliation against source systems, which banking and finance teams treat as a basic requirement.

A practical exercise is to hand over a dataset with five planted defects and ask the candidate to find them, write the checks that would have caught them, and decide which checks should block a pipeline and which should only warn. This is close to work we do ourselves: Onfocus built a cloud data monitoring and anomaly-tracking console for Neutrinos, used for enterprise banking and big data workloads.

Governance and privacy skills

The core obligations under India's DPDP Rules, including security safeguards, breach notification and retention, apply from about 13 to 14 May 2027 (Storyboard18, Nov 2025). Data engineers implement much of this in practice: tagging personal data, masking it outside production, running retention and deletion jobs, and keeping lineage good enough to trace where a record went.

Ask senior candidates how they would find every table holding one customer's personal data and delete it within a fixed deadline. The answer shows whether they treat lineage and classification as engineering work or as someone else's paperwork. Our DPDP guide for India GCCs covers roles and deadlines, and our approach to data and AI governance is on the AI and data page.

A practical assessment loop

Keep the loop short enough that good candidates finish it. Strong data engineers in India often hold more than one offer at a time.

Decide your position on AI coding assistants before you start. A Nasscom study found that about 70% of early-career tech talent is "AI-proficient" but only about 23% is "AI-native", and warned that deep engineering skills are slipping (Nasscom via YourStory, Jul 2026). We suggest allowing assistants in the exercise, then asking candidates to explain and defend each step. That tests engineering judgment, which is the scarce skill.

Seniority mix and what to pay

Experienced engineers dominate GCC demand: Quess puts the 4 to 12 years band at 56% of GCC hiring demand (Quess via Business Today, Jul 2026). For a first data team, plan for a senior or lead engineer who has run a platform in production, two or three mid-level engineers, and graduates only once standards and code review are in place.

There is no neutral, primary-sourced salary survey for data engineers by experience in India. The figures below come from different sources using different methods, so use them as reference points rather than a pay scale.

Increment estimates differ by source. TeamLease Digital puts the 2026 GCC increment at 10.4%, against Aon's 9.3% (TeamLease Digital via Telangana Today, Oct 2026). Our post on what AI and data engineers cost in India in 2026 covers the wider pay picture.

Where to look

Senior data talent is concentrated in the large metros. Quess reports that 87 to 88% of advanced-skill GCC hiring is in Tier-1 cities, although Coimbatore grew about 30% quarter on quarter (Quess via CXOToday, Nov 2026). foundit's projection for 2026 GCC hiring puts Bengaluru at 30%, Hyderabad at 15% and Tier-2 cities combined at 15%, with the Tier-2 share growing 23% year on year (foundit via Business Today, Jul 2026).

In practice, hire your platform lead and streaming specialists where the experienced pool is deepest, and consider Tier-2 cities for mid-level roles once senior reviewers are in place. Our comparison of the best cities for a GCC in India and the guide to setting up a GCC in Hyderabad cover rents and incentives.

Direct hiring, a partner, or Build-Operate-Transfer

Companies that already have an Indian entity and an experienced data leader can hire directly. Those starting from nothing face a longer path, because the entity, the recruiting pipeline and the technical bar all have to exist before the first pipeline ships. Even established GCCs mix their routes: Quess finds that 25 to 40% of roles are filled internally, and that the share met through subcontracting rose from 18% to 26% (Quess via Business Today, Jul 2026).

Under a Build-Operate-Transfer (BOT) model, a partner hires and runs the team, then transfers it to you once it is stable. The trade-offs against captive, EOR and outsourcing models are set out in BOT vs captive vs EOR vs outsourcing.

How Onfocus can help

Onfocus designs and builds data engineering and data quality systems, and recruits and runs engineering teams for clients in fintech, banking and Web3. We can shape the assessment around your stack and build the team in India, either as a managed team or under a Build-Operate-Transfer arrangement that ends with the team in your own entity. Talk to us about a data team in India.

Frequently asked questions

What skills should I test when hiring data engineers in India?

Test lakehouse table design and cost management, streaming correctness when consumers fail or data arrives late, and data quality checks and reconciliation against source systems. Governance skills such as tagging, masking and deleting personal data matter more as India's DPDP obligations approach.

How long should a data engineer interview process be?

Keep it short: a screening call, a pipeline exercise capped at about three hours, a design review, an incident walkthrough and a final conversation. Strong data engineers in India often hold more than one offer, so a long loop loses them.

How much do data engineers cost in India in 2026?

There is no neutral, primary-sourced salary band by experience. A recruitment firm puts Databricks engineers with 4 to 6 years at ₹28 lakh to ₹42 lakh a year and Snowflake engineers at ₹24 lakh to ₹36 lakh, but these figures are unverified and should be treated as indicative.

Which Indian cities are best for hiring senior data engineers?

Senior talent is concentrated in Tier-1 cities, which account for 87 to 88% of advanced-skill GCC hiring according to Quess. Tier-2 cities such as Coimbatore are growing and can suit mid-level roles once senior reviewers are in place.

Should candidates be allowed to use AI coding assistants in the assessment?

We suggest allowing them, then asking candidates to explain and defend each step of their solution. That tests engineering judgment, which is the scarce skill.