Every company wants to be "data-driven." Every CTO's strategy deck mentions data lakes, real-time analytics, and AI/ML pipelines. But between the vision and the reality sits a critical bottleneck: data engineers. These are the professionals who build and maintain the infrastructure that makes data accessible, reliable, and useful — and there are nowhere near enough of them.
In India, the demand-supply gap for data engineering roles has widened every year since 2022. Job postings for data engineers grew 45 percent year-over-year in 2025, while the supply of qualified candidates grew only 15 percent. The result is aggressive salary inflation, intense competition, and a hiring timeline that frustrates even the most patient engineering managers.
How the Role Has Evolved
Data engineering five years ago meant writing SQL queries, managing ETL pipelines in Informatica or Talend, and administering on-premises data warehouses. The role has transformed dramatically:
Modern data engineers are software engineers who specialise in data. They write production-quality Python or Scala code, design distributed systems, implement CI/CD for data pipelines, and operate cloud-native data platforms. The line between data engineering and backend engineering has blurred significantly.
The modern data stack has shifted from monolithic ETL tools to a composable ecosystem:
- Orchestration: Apache Airflow, Dagster, Prefect
- Processing: Apache Spark, Apache Flink, Apache Beam
- Streaming: Apache Kafka, Amazon Kinesis, Confluent
- Transformation: dbt (data build tool) — the tool that has arguably changed data engineering more than any other in recent years
- Storage: Snowflake, Databricks, BigQuery, Redshift
- Data quality: Great Expectations, Monte Carlo, Soda
- Cataloguing: Amundsen, DataHub, Atlan
An engineer who only knows the legacy stack (Informatica, SSIS, on-premises Hadoop) is not prepared for the modern data engineering role. This is a significant source of the supply gap — there are plenty of people with "data engineer" titles, but far fewer with the modern skill set companies actually need.
Why Supply Cannot Keep Up
1. Education Lag
Indian engineering curricula still teach data management through the lens of relational databases and classical data warehousing. Apache Spark, Kafka, dbt, and cloud-native data platforms are rarely covered in formal education. Graduates entering the workforce need 12-18 months of on-the-job learning before they are productive in a modern data engineering role.
2. Competing Demand from Adjacent Roles
Data engineers are courted by multiple industries and role types simultaneously. The same skills that make someone a good data engineer also qualify them for:
- Machine Learning Engineer roles (which often pay 15-20 percent more)
- Platform Engineer roles at cloud providers
- Analytics Engineer roles (a new category enabled by dbt)
- Solutions Architect roles at data platform vendors (Snowflake, Databricks, Confluent)
This means data engineering talent has more exit options than almost any other IT specialisation, further thinning the available pool for traditional data engineering positions.
3. GCC Absorption
Global Capability Centers in India have been aggressively building data and AI teams. Companies like JP Morgan, Goldman Sachs, Walmart, and Target have data engineering teams of 200-500 people in India. GCCs pay at the top of market and offer the appeal of working on global-scale data problems, making them formidable competitors for talent.
4. The Seniority Problem
Companies rarely need junior data engineers. The role requires understanding of distributed systems, data modelling, pipeline reliability, and business context — skills that take 3-5 years to develop. The market is saturated with candidates who have 0-2 years of experience and starved for candidates with 4-8 years.
Salary Trends (India, 2026)
Based on placement data from our niche recruitment practice:
- Junior Data Engineer (0-2 years): 8-15 LPA
- Mid-level Data Engineer (3-5 years): 18-30 LPA
- Senior Data Engineer (5-8 years): 30-50 LPA
- Staff/Principal Data Engineer (8+ years): 50-75 LPA
- Data Engineering Manager/Director: 55-90 LPA
These figures represent a 15-25 percent increase over 2024 levels for mid-to-senior roles. Candidates with experience in Databricks, Snowflake, and real-time streaming (Kafka/Flink) command premiums at the top of these ranges.
Hiring Strategies That Work
1. Screen for Fundamentals, Not Specific Tools
The modern data stack changes every 18 months. dbt did not exist five years ago. Databricks has reinvented itself twice. Hiring for specific tool proficiency is short-sighted.
Instead, screen for:
- SQL mastery — Still the lingua franca of data. A candidate who writes efficient, readable SQL with proper CTEs, window functions, and performance awareness will learn any tool on top
- Python or Scala proficiency — Not notebook-level scripting, but production-quality code with proper error handling, testing, and modular design
- Distributed systems thinking — Understanding of partitioning, parallelism, data skew, idempotency, and exactly-once versus at-least-once processing
- Data modelling — Dimensional modelling, data vault, or modern approaches like the dbt modelling paradigm. The ability to design schemas that serve both analytics and operational needs
- Engineering practices — Version control, CI/CD, testing (unit tests for transformations, data quality checks), documentation, and monitoring
2. Build a Training Pipeline
Given the supply shortage, waiting for perfectly qualified candidates is a losing strategy. Instead, identify adjacent talent that can be upskilled:
- Backend engineers with strong Python and systems design skills can transition to data engineering with 3-6 months of focused training
- Data analysts with deep SQL and business domain knowledge can be upskilled on engineering practices and modern tools
- DevOps engineers already understand CI/CD, infrastructure automation, and cloud platforms — add data-specific tooling and they are effective quickly
Invest in structured training programmes, pair new data engineers with senior mentors, and accept a 3-6 month ramp-up period. The cost of training is a fraction of the premium you would pay for an immediately-available senior candidate.
3. Consider Contract and Augmentation Models
If you need data engineering capacity now but want to build the permanent team over time, staff augmentation provides immediate capacity while your permanent staffing pipeline develops. Many of our clients start with 2-3 augmented data engineers to handle immediate project needs and then convert the best performers to permanent roles.
4. Compete on the Work, Not Just the Salary
Top data engineers choose roles based on:
- The data challenges they will solve (scale, complexity, real-time requirements)
- The modern-ness of the data stack (nobody wants to maintain a legacy Informatica ETL)
- The team's engineering culture (code review for data pipelines, CI/CD, data quality standards)
- Learning opportunities (conference budgets, training allowances, exposure to new tools)
- Autonomy and ownership
If your data infrastructure is modern and your engineering culture is strong, lead with that in the job description and interviews. If your stack is legacy, be honest about the modernisation roadmap and position the role as an opportunity to lead the transformation.
5. Engage a Specialist Recruiter
Generalist recruiters struggle with data engineering because they cannot differentiate between a candidate who has used Spark in a tutorial and one who has operated a 100-node Spark cluster in production. Our niche recruitment team includes former data professionals who conduct technical pre-screens, evaluate real-world project experience, and present only candidates who meet your technical bar.
The Outlook
The demand-supply gap for data engineering talent is not closing anytime soon. AI adoption is creating more data infrastructure requirements, not fewer. Companies that succeed in hiring data engineers will be those that invest in training pipelines, offer compelling technical challenges, move quickly in the hiring process, and work with specialist recruitment partners who understand the domain.
If data engineering hiring is a pain point for your organisation, reach out to our team. We have been placing data engineers across product companies, GCCs, and tech-forward enterprises for years, and we understand what it takes to attract and secure this talent.
