Data Engineer CV — example, template & tips for India 2026
Build an ATS-optimised data engineer CV showcasing your pipeline, orchestration, and warehouse skills. For data engineering roles at Flipkart, Swiggy, Razorpay, and analytics consulting firms across India.
Salary range (India)
₹6L – ₹45L depending on tools and experience
Top hiring companies
Flipkart · Amazon India · Swiggy · Zomato · PhonePe
What to include in a Data Engineer CV
- Pipeline architecture: tools used (Spark, Kafka, Airflow), data volume processed (TB/day or events/second)
- Data warehouse or lakehouse: Snowflake, BigQuery, Databricks, Redshift — with scale context
- Transformation tools: dbt models built, transformation logic, data quality checks implemented
- Orchestration: Airflow DAGs managed, scheduling complexity, failure handling
- Programming: Python and SQL — show advanced SQL (window functions, CTEs, partitioning)
- Data quality and observability: Great Expectations, Monte Carlo, dbt tests
Common Data Engineer CV mistakes
- Confusing data engineer and data analyst scope — engineers build systems, analysts consume them; make the distinction clear
- No pipeline scale context — "built ETL pipelines" vs "built pipelines processing 800GB daily across 12 data sources"
- Missing modern data stack tools — dbt, Airflow, and a cloud warehouse are baseline for most 2026 data engineering JDs
- Not quantifying latency or throughput — near-real-time vs batch processing signals very different skills
- Omitting data quality work — ATS and hiring managers both look for reliability and observability experience
Top skills for a Data Engineer CV
These keywords are heavily screened in ATS for data engineer roles. Make sure they appear naturally in your CV.
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Check my keyword score freeData Engineer CV — frequently asked questions
What is the difference between a data engineer and a data scientist in India, and which pays more?
Data engineers build and maintain the infrastructure that delivers clean, reliable data — pipelines, warehouses, orchestration systems. Data scientists consume this data to build models, run experiments, and generate insights. In India in 2026, senior data engineers with deep distributed systems experience (Spark, Kafka, Databricks) often match or exceed data scientist salaries, particularly at product and fintech companies. Data scientists with ML deployment and production model experience tend to earn more at AI-first companies. Both tracks are well-compensated relative to general software engineering, with senior salaries in the ₹30L–₹60L range at top-tier companies.
What are the most important skills for a data engineer to have in India in 2026?
The modern data engineering stack in India centres on: Python for pipeline code, SQL for transformation logic, Apache Spark or Databricks for large-scale processing, Apache Airflow for orchestration, dbt for transformation layer management, and a cloud data warehouse (Snowflake or BigQuery). Kafka or Kinesis is increasingly expected for streaming pipelines. Cloud platform proficiency (AWS or GCP) is effectively mandatory. Data quality tooling (dbt tests, Great Expectations) is becoming a strong differentiator. Candidates who can design end-to-end data architectures — not just implement individual pipelines — command significant salary premiums.
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