Data Engineer Interview Questions: SQL, Python, Pipelines and Resumes

Senior data engineer interview questions cover SQL window functions, Python scripting, pipeline idempotency, data modelling and behavioural rounds using the STAR method. Most interviews at companies like Amazon, Flipkart and Razorpay test your ability to reason through failures and trade-offs, not just recall syntax. Rounds typically include CTEs, Spark design and backfill strategies.

• SQL rounds go deep on window functions, joins, deduplication and query optimisation, not just SELECT basics.
• Python questions focus on generators, file handling and API ingestion patterns rather than algorithms alone.
• Pipeline design questions probe idempotency, retries, backfills and late-arriving data handling.
• Your resume needs measurable outcomes, not job descriptions, and every project needs a “reduced by X%” or “processed Y GB daily” claim.
• Behavioural rounds at FAANG and Indian unicorns both use the STAR method, so practise it explicitly.

SQL Interview Questions for Senior Data Engineers: What Gets Tested and How Deep

SQL is the first filter in almost every senior data engineer interview. According to a 2024 StrataScratch analysis of over 800 real interview questions submitted by candidates, SQL accounted for roughly 45% of all technical questions asked in data engineering interviews, with window functions appearing in more than 60% of those SQL rounds.

The most common SQL interview questions for data engineer roles start with joins but do not stay there. Interviewers expect you to explain the difference between a LEFT JOIN and a LEFT ANTI JOIN, and to know when a CROSS JOIN is actually the right tool. They will give you a table with duplicate rows and ask how you would deduplicate using ROW_NUMBER() partitioned over a business key.

Window Functions and CTEs

Window functions are where mid-level candidates stumble. Know RANK(), DENSE_RANK(), ROW_NUMBER(), LAG(), LEAD() and running totals with SUM() OVER(). Be ready to explain the difference between RANK and DENSE_RANK with a concrete example, not just a definition.

CTEs come up because they make query logic readable and testable. Interviewers want to see you use a CTE to isolate a subproblem, then join back to the main table. Recursive CTEs appear in senior rounds, particularly for hierarchical data like org charts or category trees. Our Big Data Analytics notes cover query optimisation patterns in more depth if you want to go further.

Query Optimisation Questions

A common prompt: “This query runs in 40 seconds on a 500M row table. Walk me through how you would optimise it.” The interviewer wants to hear about partition pruning, avoiding SELECT *, checking for missing indexes, and understanding whether the bottleneck is I/O or compute. In Spark-based environments, they will also ask about broadcast joins and shuffle partitions.

SQL Topic	Frequency in Interviews	Depth Expected
Window Functions	High (60%+ of SQL rounds)	Write from scratch, explain partitioning
CTEs	High	Chained CTEs, recursive CTEs
Joins (all types)	Very High	Anti joins, self joins, performance impact
Deduplication	Medium-High	ROW_NUMBER() approach, DISTINCT trade-offs
Query Optimisation	Medium (senior roles)	Execution plans, partitioning, indexes
Star vs Snowflake Schema	Medium	Trade-offs, when to use each

Star schema versus snowflake schema is a standard data modelling question in senior data engineer interview questions. The short answer: star schema denormalises dimensions for faster read performance, while snowflake normalises them to reduce storage and maintain referential integrity. In a Redshift or BigQuery context, star schema almost always wins for analytical workloads. Know why.

Python and Pipeline Design: Senior Data Engineer Interview Questions Explained

Python interview questions for data engineer roles are less about LeetCode and more about real-world scripting. A LinkedIn Jobs analysis from early 2025 found Python listed as a required skill in over 78% of data engineering job postings in India, making it the single most requested technical skill ahead of even SQL.

Python Questions You Will Actually Face

File handling comes up constantly. You will be asked to read a 10 GB CSV without loading it into memory. The answer involves generators or chunked reading with pandas, and you should be able to explain why a generator is memory-efficient: it yields one row at a time rather than building the full list in RAM.

API ingestion questions test pagination handling, rate limiting and error recovery. A typical prompt: “Write a function to pull data from a paginated REST API and store results incrementally.” They want to see you handle HTTP errors gracefully, implement exponential backoff and write results to a staging location before any transformation runs. If you are preparing to shift from data science to AI and ML, these scripting patterns transfer directly.

Pipeline Design: Idempotency, Retries and Late Data

This is where senior data engineer interview questions separate the experienced from the theoretical. Idempotency means running your pipeline twice produces the same result as running it once. Interviewers will ask how you would design a pipeline that is safe to retry. The answer involves writing to a staging table keyed on a run ID, then doing an upsert, never a blind append.

Backfill questions are common at companies running Airflow. You will be asked how you would reprocess 90 days of historical data without duplicating records or overwhelming downstream systems. The right answer involves a backfill DAG with date-partitioned tasks, idempotent writes and rate controls. Airflow’s catchup parameter and execution_date context are things you need to know cold.

Late-arriving data is a Spark Structured Streaming and Kafka-adjacent topic. Interviewers want to hear about watermarks: you define a threshold beyond which late records are dropped, balancing completeness against latency. A 10-minute watermark in a streaming pipeline is a reasonable starting point for discussion. According to Databricks’ 2024 State of Data and AI report, over 65% of enterprise data teams reported late-arriving data as a top operational challenge in streaming pipelines.

Amazon interview questions for data engineer roles tend to layer in Leadership Principles on top of the technical round. You will be asked to describe a time you improved pipeline reliability or reduced data latency. Use the STAR method: Situation, Task, Action, Result, and always quantify the result. “Reduced pipeline failure rate by 40%” lands better than “improved reliability.” The AI job market and skills piece on this site has useful context on what FAANG-adjacent companies are prioritising right now.

Data Engineer Resume, Projects and Behavioural Rounds

Your data engineer resume has one job: prove you have built things that worked at scale. Recruiters spend an average of 7.4 seconds on an initial resume scan, according to a Ladders eye-tracking study. That means your measurable outcomes need to be visible in the first third of the page.

Resume Structure That Gets Shortlisted

Lead with a two-line professional summary that names your stack and your scale. Then list experience in reverse chronological order with bullet points that follow this pattern: action verb, what you built, measurable outcome. “Designed a Spark ETL pipeline processing 3 TB daily, reducing report latency from 6 hours to 45 minutes” is the format. Avoid passive constructions like “was responsible for.”

Your skills section should list technologies honestly. Separate languages (Python, SQL, Scala) from frameworks (Spark, Airflow, dbt) and cloud platforms (AWS Glue, GCP Dataflow, Azure Data Factory). Interviewers will probe whatever you list, so do not pad it. If you have only used Kafka in a tutorial, do not list it under production experience.

Projects and Portfolio for Senior Data Engineer Interview Preparation

If you are a fresher or career switcher, projects carry the weight that experience would for a senior candidate. Build an end-to-end pipeline: ingest from a public API, transform with Python or Spark, load into a warehouse like BigQuery or Snowflake, and visualise with a simple dashboard. Put it on GitHub with a clear README. The bootcamp training programs at 3.0 University are structured around exactly this kind of project-first learning. You can also read through learner success stories to see how others have built portfolios that got them hired.

Behavioural Rounds

Behavioural questions in senior data engineer interviews are not soft fillers. At Amazon they are scored against Leadership Principles with the same rigour as technical questions. Prepare five strong STAR stories covering: a pipeline failure you diagnosed and fixed, a time you pushed back on a bad technical decision, a project where you had to work with incomplete data, a time you improved a process without being asked, and a cross-functional collaboration. That bank covers 90% of what you will face. The REACH learner community runs regular mock interview sessions where you can practise these with peers.

If you are aiming for senior roles at Indian companies like Swiggy, PhonePe or Meesho, also prepare to discuss data modelling decisions: why you chose a star schema over a snowflake schema for a specific workload, or why you picked a partitioned table over a clustered one. These questions reveal whether you think in trade-offs, which is what senior roles demand. Checking out 3.0 University’s online certification courses can help you fill gaps in these areas before your next round.

The field is shifting fast. Whether you are preparing for a role at a Bengaluru startup or a global company like Amazon or Google India, the fundamentals remain the same: SQL depth, pipeline reliability, Python scripting and the ability to communicate trade-offs clearly. Start practising with real datasets, not toy examples, and time yourself on SQL problems without autocomplete.

Frequently Asked Questions

What are the most common senior data engineer interview questions?

Senior data engineer interview questions typically cover SQL window functions, CTEs, Python scripting, pipeline idempotency, backfill strategies, data modelling and system design. Behavioural rounds using the STAR method are also standard, particularly at FAANG companies and Indian unicorns like Flipkart, Razorpay and PhonePe.

Which SQL questions come up most often in data engineer interviews?

Window functions (ROW_NUMBER, RANK, LAG/LEAD), CTEs including recursive ones, all join types especially anti joins, deduplication strategies, and query optimisation using execution plans and partitioning. StrataScratch data from 2024 shows window functions appear in over 60% of SQL interview rounds for data engineering roles.

What Python questions do senior data engineer interviews include?

Expect questions on reading large files with generators or chunked pandas, building API ingestion scripts with pagination and error handling, writing idempotent ETL functions, and using libraries like requests, boto3 or google-cloud-storage. Interviewers care about memory efficiency and fault tolerance, not just whether the code runs.

How should a data engineer resume be structured?

Start with a two-line summary naming your stack and scale. List experience with bullet points in the format: action verb, what you built, measurable outcome. Separate your skills into languages, frameworks and cloud platforms. Quantify everything: data volumes processed, latency reductions, failure rates improved. Projects with GitHub links are essential for freshers and career switchers.

How do I prepare for a system design round in a senior data engineer interview?

Study idempotency, partitioning strategies, schema design and pipeline orchestration with Airflow. Be ready to design a pipeline from ingestion to serving layer, discuss trade-offs between batch and streaming, and explain how you would handle late-arriving data with watermarks. Practise drawing architecture diagrams and articulating your reasoning out loud before the interview.

What are senior data engineer interview questions asked at Indian companies like Amazon India and Flipkart?

Indian unicorn and FAANG India interviews combine technical SQL and Python rounds with behavioural questions scored against company values. Amazon India specifically tests Leadership Principles alongside pipeline design. Flipkart and Razorpay focus on large-scale data processing, Spark optimisation and real-time streaming. Quantified STAR stories are essential for clearing these rounds.

Last updated: June 2025. Reviewed by the 3University editorial team.