Data engineering is one of the fastest-growing roles in Indian tech. Product companies, fintech firms, and large enterprises are all hiring data engineers to build the pipelines that power analytics, ML, and decision-making. This guide covers the specific data engineering interview questions asked at Indian product companies and the broader tech ecosystem.
SQL and Data Modeling
SQL remains the core language of data engineering: tested at every level. Beyond SELECT and JOIN, interviewers test: window functions (ROWNUMBER, RANK, DENSERANK, LAG, LEAD, PARTITION BY): these appear in almost every senior data engineering interview. CTEs (Common Table Expressions): recursive CTEs for hierarchical data. Performance optimisation: explain plans, index strategies, query rewriting. Data modelling: star schema (fact tables + dimension tables) vs snowflake schema (normalised dimensions). SCD (Slowly Changing Dimensions) Type 1 (overwrite), Type 2 (new row), Type 3 (previous value column). Partitioning and bucketing in distributed storage.
Apache Spark and Distributed Processing
Spark is the dominant distributed processing framework tested at Indian product companies. Core concepts: RDD vs DataFrame vs Dataset. Lazy evaluation: transformations (map, filter, groupBy) are lazy; actions (count, collect, write) trigger computation. Partitioning: repartition (shuffles data), coalesce (reduces partitions without full shuffle). Caching: persist() and cache(): when and why. Broadcast joins: for small tables (< a few hundred MB) to avoid shuffle. Common interview scenarios: handling data skew (salting technique), optimising Spark jobs that run slowly, reading from and writing to Parquet, Hive, and Delta Lake.
Data Pipeline Design (Apache Airflow)
Airflow is the most common orchestration tool tested in Indian data engineering interviews. Key concepts: DAG (Directed Acyclic Graph): the core abstraction, collection of tasks with dependencies. Operators: PythonOperator, BashOperator, SparkSubmitOperator, BigQueryOperator, S3Operator. Task dependencies: >> and << operators. XComs: passing small data between tasks. Sensors: waiting for external conditions (file arrival, API response). Scheduling: cron expressions in Airflow. Common design questions: how to handle late-arriving data, backfill strategies, idempotent pipelines (safe to re-run), circuit breakers for upstream failures.
Data engineering interviews test both technical depth and system design. Practise with HireStepX's AI mock interviewer before your next round.
Practice freeCloud Data Stacks
Modern data engineering at Indian companies typically uses cloud data stacks. AWS: S3 (storage), Glue (ETL), Redshift (data warehouse), Kinesis (streaming), Athena (serverless SQL). GCP: GCS (storage), Dataflow (Apache Beam), BigQuery (serverless data warehouse), Pub/Sub (streaming). Azure: ADLS (storage), ADF (pipeline orchestration), Synapse Analytics (data warehouse). dbt (data build tool): SQL-first transformation layer, increasingly tested. Growing in Indian companies as the standard for transform layer. Delta Lake / Apache Iceberg: ACID transactions on data lakes: enables upserts and time travel on large datasets. Expected knowledge at senior levels.
Frequently asked questions
Practice these questions on HireStepX
Continue reading
All posts →
SQL Interview Questions for Indian Companies 2026
Top SQL interview questions for Indian IT and product companies in 2026. Window functions, joins, subqueries, query optimisation, and the tricky SQL scenarios that interviewers actually ask.
HireStepX Team · 11 min read
Machine Learning Interview Questions for India 2026
Top machine learning interview questions for Indian companies in 2026. ML fundamentals, algorithms, evaluation metrics, feature engineering, deployment, and case study questions at product and AI companies.
HireStepX Team · 12 min read