Overview
Data engineers build the pipelines, warehouses, and infrastructure that make analytics and ML possible. Without them, Data Scientists and ML Engineers have no clean data to work with. Demand has grown sharply alongside the AI/ML boom: every Indian product company, every fintech, every health-tech needs robust data platforms. Average salaries trail AI/ML engineers by 10 to 20 percent but the role is more stable and the supply is also tighter.
Day-to-day responsibilities
Design and maintain ETL/ELT pipelines using Apache Airflow, Dagster, or dbt; manage data warehouses (Snowflake, BigQuery, Redshift); build streaming pipelines with Kafka, Flink, or Spark Streaming; ensure data quality through automated tests and observability tools (Monte Carlo, Great Expectations); collaborate with analysts and ML engineers on data modelling; optimise query performance and warehouse costs; maintain data catalogs and lineage.
Skills required
- SQL (advanced, including window functions and CTEs)
- Python or Scala
- Airflow or Dagster orchestration
- dbt for transformations
- Snowflake / BigQuery / Redshift
- Spark or Flink
- Kafka or Pulsar
- Data modelling (Kimball, Inmon)
Education path
B.Tech CSE / IT, BCA + MCA, or M.Sc Computer Science. Data engineering specialisation is increasingly taught at master's level (M.Sc Data Engineering at private deemed universities). Self-taught entry from analytics or backend engineering backgrounds is common.
Career progression
- 1Data Engineer (0-2 yrs)
- 2Senior Data Engineer (2-5 yrs)
- 3Staff Data Engineer (5-9 yrs)
- 4Principal Data Engineer (9+ yrs)
- 5Data Platform Architect / Director
Top employers in India
Useful certifications
- Google Cloud Professional Data Engineer
- AWS Data Analytics Specialty
- Snowflake SnowPro Core
- Databricks Data Engineer Associate
- dbt Analytics Engineering