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Data engineering interview prep for engineers in India

PrepHike · 5 min read

Data engineering pays well in India, but the interviews are wide. A panel can move from a window function to a Snowflake cost question to a slowly changing dimension in ten minutes. This guide covers what gets tested, with concrete examples, so you can prep the right things instead of everything.

Why data engineering interviews feel harder than they should

The job sounds focused: move data, model it, keep it clean. The interview is not focused. A single round can touch SQL depth, Python scripting, a warehouse like Snowflake or BigQuery, an orchestrator like Airflow, a transformation layer like dbt, and then turn to design questions about how you would build a pipeline that does not break at 3 AM. Most engineers in India who are underpaid are not weak at the work. They are unprepared for the breadth and the framing of the questions, so they sound junior even with four years of real pipelines behind them.

This article walks through what each area probes, with examples you can practice against. If you are not sure where your real market value sits before you start, read the audit underpaid engineers skip first, because that decides how aggressive your prep needs to be.

SQL: where most candidates quietly lose points

SQL is the floor, not a bonus. A strong sql interview for a data engineer goes past joins fast. Expect window functions, conditional aggregation, and questions that test whether you understand execution, not just syntax.

A common one: "Find the second highest salary per department." Many people reach for a correlated subquery. Better answers use DENSE_RANK() OVER (PARTITION BY dept ORDER BY salary DESC) and explain why DENSE_RANK versus RANK versus ROW_NUMBER matters when ties exist. Another favorite: "Find users active on three or more consecutive days." This is a gaps-and-islands problem, and the clean solution uses a date minus a row number to form a group key. If you can talk through that, you read as senior.

Also be ready for the "why is this query slow" question. Know what a full table scan is, when an index helps, why SELECT * on a wide table hurts, and what happens when you filter on a function-wrapped column. Panels probe whether you reason about data volume. A query that works on 10,000 rows and dies on 200 million is the real interview.

Python: scripting, not LeetCode

Data Python is different from algorithm Python. You will get file parsing, deduplication, flattening nested JSON, calling an API with pagination and retries, and writing to a warehouse in batches. Expect questions about generators for memory, why you would stream a large file instead of loading it, and how you handle a record that fails validation without killing the whole job. Pandas comes up, but the better signal is whether you know when pandas is the wrong tool because the data does not fit in memory.

Snowflake and warehouse design

If the role lists Snowflake, the Snowflake interview portion tests whether you understand its architecture, not just its SQL. Be ready to explain the separation of storage and compute, what a virtual warehouse is, and why you can scale compute up for a heavy load and back down without touching the data. Know micro-partitions and how clustering keys help on large tables. Cost questions are common in India-based roles because teams watch credit burn closely: explain auto-suspend, warehouse sizing, and why a poorly written query on an oversized warehouse is an expensive mistake.

Other likely topics: the difference between a transient, temporary, and permanent table, how Time Travel works, what zero-copy cloning is good for, and how you would load data using COPY INTO from a stage. If the stack is BigQuery or Redshift instead, the architecture story changes but the framing is identical: storage, compute, partitioning, cost.

Dimensional modeling and SCD

This is where experience shows. Panels ask you to model a real scenario, say orders, products, and customers, into facts and dimensions. Know the difference between a star schema and a snowflake schema and when each is acceptable. Be able to define grain, because a fact table without a clear grain is a bug waiting to happen.

Slowly changing dimensions are almost guaranteed. Be precise: Type 1 overwrites and loses history, Type 2 adds a new row with effective dates and a current flag to keep full history, Type 3 keeps a previous-value column for limited history. The follow-up is the real test: "A customer changes their city. Which SCD type, and why?" Your answer should turn on whether the business needs to report on past sales by the old city. That is the difference between reciting definitions and modeling for a business.

Airflow, dbt, and ETL versus ELT

Orchestration questions probe reliability thinking. For Airflow, know DAGs, tasks, operators, what idempotency means and why a task must be safe to re-run, how backfills work, and how you handle a failed task without corrupting downstream data. A sharp answer mentions partition-based reruns rather than reprocessing everything.

For dbt, expect models, refs, tests, and the role of incremental models. Be ready to explain why dbt pushed the industry from ETL toward ELT: load raw data into the warehouse first, then transform with SQL where the compute and the data already live. The trade-off discussion, when ETL still makes sense for heavy pre-load cleaning or compliance, is what separates strong candidates. Practicing these as short, structured answers is exactly what a skill Q&A bank is for.

Data quality: the topic that signals seniority

Junior engineers build pipelines. Senior engineers build pipelines that fail loudly and correctly. Expect questions on null handling, duplicate detection, schema drift, freshness checks, and what you do when source data silently changes shape. Know the dbt test types, generic and singular, and the idea of contracts. When you can describe a real incident, what broke, how you caught it, how you stopped it recurring, you sound like someone worth a higher band. Frame that story well using the approach in the 30-second project method.

The design round

The final test is usually open: "Design a pipeline to ingest clickstream data and make it queryable for analysts." There is no single answer. They watch how you scope it: batch or streaming, ingestion, staging, transformation, modeling, orchestration, monitoring, and cost. Talk trade-offs out loud. Our system design checklist maps cleanly onto data pipeline design.

How to prep without boiling the ocean

You cannot master all of this in two weeks, and you do not need to. Match prep to the job description, drill the three or four areas it emphasizes, and do at least one mock so the breadth stops surprising you mid-round. If you want a structured plan and honest feedback on where you actually stand, that is what our method is built around, and the pricing is tied to the hike, not the hours.