Why Data Engineering Roles Stay Vacant Longer Than Any Other Tech Hire

There is a particular kind of open requisition that every engineering leader eventually recognises. It was approved in February with real urgency behind it. It is still open in June. Two analytics projects are quietly waiting on it. Nobody has done anything obviously wrong, and yet the seat is empty and the meter has been running for four months.

More often than not, that req is for a data engineer.

This is not a perception problem. According to SHRM's 2025 Talent Acquisition Benchmarking Report, engineering roles take an average of 62 days to fill, the longest of any occupation tracked, and senior positions push well past that. Layer the specific scarcity of data engineering onto that baseline and the timeline stops looking like a benchmark and starts looking like a business risk. The reasons why are worth unpacking, because most of them are fixable and most companies are fixing the wrong ones.

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The Supply Problem Is Real, and It Is Structural

Nasscom's "State of Data Science & AI Skills in India" report, produced with Salesforce and Draup, puts the overall demand-supply gap for data and AI professionals in India at roughly 51 percent. That alone would be uncomfortable. What makes it worse for anyone trying to hire data engineers in India where the gap concentrates. The same report finds that certain roles carry a demand-supply disparity between 60 and 73 percent, with ML Engineer, Data Scientist, Data Architect and DevOps Engineer sitting at the sharp end. Data Engineer and ML Engineer between them account for a large share of installed talent, which sounds reassuring until you realise the same two roles absorb the bulk of the demand too.

So the pool is not empty. It is fully spoken for. Every company running a serious data platform is fishing the same water, and the fish already have jobs they are reasonably happy in.

The Role Ate Three Other Jobs and Nobody Updated the Job Description

Here is the part that catches companies off guard.

Ten years ago, a data engineer wrote ETL jobs and kept the warehouse loaded. Today the same title is expected to cover distributed systems, cloud cost economics, streaming architecture, data governance and, increasingly, the infrastructure that AI workloads run on. When an organisation says its AI project is stalled, the honest diagnosis is usually that the data layer underneath it is slow, messy or untrustworthy. The scope expanded. The job description did not.

That expansion is exactly why data pipeline hiring produces such a strange funnel shape. Applications arrive in volume. The pipeline narrows brutally at the technical screen, because plenty of candidates can write a transformation script and far fewer can reason about why a job fails at 3am under load, what it costs to run, and whether the schema will survive the next six months of product changes.

Know Which Role You Are Actually Hiring For

Data engineering is not one job. Treating it as one is the most common reason a search drags and then produces the wrong person anyway. The distinct roles under the umbrella need different tests, different pay and different sourcing:

Data engineers who build and maintain the pipelines that move and transform data day to day

Analytics engineers who model raw data into something the business can actually query, usually living in dbt and SQL

Cloud data engineers who build on a specific stack, Azure Data Factory and Synapse, AWS Glue and Redshift, or GCP Dataflow and BigQuery, where platform depth matters as much as pipeline skill

Data platform engineers who own the underlying infrastructure, orchestration and tooling other teams build on

Data architects who design the overall system and justify the trade-offs across storage, compute and governance

Streaming and real-time engineers working in Kafka, Flink or Spark Streaming, where latency is the whole problem

DataOps and reliability engineers who keep pipelines observable, tested and alive in production

Big data engineers handling scale problems that break conventional tooling

Data governance and quality specialists, the ones whose absence you only notice during an audit

Three stakeholders picturing three different jobs while one req sits unposted is not a hypothetical. It is where a large chunk of that 62 day average quietly gets spent, before a single candidate has been contacted.

Why the Screening Stage Is Where Searches Go to Die

Interviews for these roles tend to test the wrong thing. Tool trivia is easy to ask and easy to prepare for. "Do you know Airflow" tells you almost nothing about whether someone can design a pipeline that degrades gracefully when an upstream source goes silent.

The signal sits underneath the tooling. Can the candidate reason about idempotency, backfills, schema evolution and failure modes? Have they operated a system at genuine scale, recently, or did they touch it two years ago on a project someone else architected? Recency matters more here than in most disciplines, because the stack has shifted meaningfully in the last three years.

The reliable test is a messy, realistic scenario rather than a quiz. Hand someone a pipeline that needs redesigning, with real constraints on cost and latency, and watch how they think. It surfaces genuine capability regardless of pedigree, and it rescues strong candidates who would otherwise be filtered out by a resume screen looking for the right logo.

The Compensation Trap

Data infrastructure hiring has a pricing problem that compounds the scarcity. Because the role sits between software engineering, analytics and platform work, companies frequently benchmark it against the wrong comparator. They price it like a backend engineer, or worse, like a data analyst with extra steps.

The market does not agree. Scarce skills cost more, and the premium sits precisely where the demand-supply disparity is widest: streaming, cloud cost optimisation, governance at scale and the AI-adjacent infrastructure work that everyone suddenly needs. A company anchoring to last year's band keeps producing shortlists that evaporate at the offer stage, and usually blames candidate quality rather than its own numbers.

What Actually Shortens the Search

The companies filling these roles well share a few unglamorous habits.

They define the specific role before sourcing, not during. They cut the interview loop to the rounds that genuinely predict performance, because every extra round adds calendar without adding signal. They test reasoning through real problems rather than tool recall. They benchmark pay to the specialisation, not to a generic engineering band. They build pipelines of candidates before the seat opens, since the fastest fill is the one where you already know three people. And they move decisively, because strong candidates in this market hold multiple offers and stay available for days, not weeks.

None of that is clever. All of it works.

Data engineering req open for months? Talk to Prism HRC.

Where Prism HRC Fits In

At Prism HRC, we see the data engineer talent shortage from the side the benchmark reports do not show: the search itself, and where it jams. Rarely is it one dramatic failure. It is a vague req, a loop with two rounds too many, a comp band set by someone who has never hired this role, and a shortlist built from whoever happened to be actively looking.

We work the front of the search instead. We help clients define which role they actually need before anything gets posted, screen for demonstrated depth in pipeline design, orchestration and production reliability rather than keyword matches, and reach the engineers who are doing good work somewhere else and not browsing job boards. We keep our benchmarks current and we move at the pace this market demands, because a clear, fast process closes candidates that a slow, well-funded one loses.

The scarcity is real and it is not easing soon. But a four month vacancy is rarely a market problem. It is usually a process problem wearing a market problem's clothes.