VIEWS Data Engineer Interview: Questions, Experience & Prep (2026)

VIEWS is currently hiring for 40 Data Engineer positions, making it one of the more active companies for this role right now. The interview process at VIEWS typically spans a few rounds covering SQL, Python, pipeline architecture, and behavioural questions, with an additional system design discussion for senior candidates.

The broader Data Engineer market in India is strong: knok job radar tracked 542 active listings as of July 2026. Bangalore leads with 92 openings, followed by Delhi at 66, Hyderabad and Pune at 23 each, Chennai at 14, and Mumbai at 8.

Salary ranges by experience (India market, knok data):

This guide covers the questions candidates report seeing most often at VIEWS, sample STAR answers you can adapt, and a focused preparation plan to help you move through each round with confidence.

Candidates who have interviewed at VIEWS for Data Engineer roles typically report questions across three areas: core technical skills, pipeline design, and past experience. Here are the most commonly surfaced questions:

1. Walk me through a data pipeline you built from scratch. What tools did you choose and why?
2. What is the difference between batch and stream processing? When would you pick one over the other?
3. How do you handle late-arriving or out-of-order data in a streaming pipeline?
4. Write a SQL query to find the second-highest value in a column without using LIMIT or a ranking function.
5. How do you ensure data quality at each stage of an ETL or ELT pipeline?
6. Describe your experience with Apache Spark. How have you tuned a Spark job for a large dataset?
7. How would you design a star schema or data warehouse for a retail analytics use case?
8. What is the difference between incremental and full loads? When do you use each?
9. How do you handle schema evolution in a data lake without breaking downstream consumers?
10. Describe a time a pipeline failed in production. How did you debug and fix it?
11. What orchestration tools have you used? How do you handle retries and task dependencies?
12. How would you design a system to deliver real-time sales metrics to a dashboard, end to end?

Use these as a starting point and adapt them to your own experience. Each follows the STAR structure.

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Q: Walk me through a data pipeline you built from scratch. What tools did you choose and why?

*Situation:* Our marketing team was pulling reports manually from three different data sources each week. The process took about two days and regularly produced inconsistencies between reports.

*Task:* I was asked to automate this into a reliable, scheduled pipeline that delivered clean, joined data to a reporting dashboard.

*Action:* I designed an ELT pipeline using Python for extraction, dbt for transformations, and Airflow for scheduling. Data landed in S3 from a CRM API, a Postgres database, and a Google Sheets export. I wrote dbt models to standardize and join the datasets, added dbt tests for data quality checks, and configured Slack alerts on job failures.

*Result:* The two-day manual process became a fully automated overnight job. The marketing team had fresh, reliable data every morning, and we surfaced three data quality issues in the first month that had been going unnoticed before.

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Q: Describe a time a pipeline failed in production. How did you debug and fix it?

*Situation:* A critical daily report for the finance team was missing data for two days before anyone flagged it.

*Task:* I had to find the root cause quickly, restore the missing data, and prevent it from happening again.

*Action:* I started with Airflow logs. The extraction step had succeeded, but the transformation step was throwing a silent schema mismatch error because it was misconfigured to not raise on failure. The upstream team had added a column to the source table without notifying us. I patched the transformation to handle the new column, backfilled the two days of missing data using a parameterized DAG run, and added an automated schema drift detection check to alert on any future upstream changes.

*Result:* The finance report was restored within three hours of me picking it up. The schema detection check caught two more upstream changes in the following month before they caused any disruption.

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Q: How do you ensure data quality at each stage of an ETL or ELT pipeline?

*Situation:* At a previous role, downstream dashboards would silently show wrong numbers when upstream data changed, which eroded trust with business stakeholders.

*Task:* I was asked to build a data quality framework into our pipeline so issues were caught before data reached consumers.

*Action:* I introduced a layered approach. At ingestion, I added row count checks and null checks on key fields. At transformation, I used dbt tests for uniqueness, referential integrity, and accepted value ranges. At the serving layer, I set up anomaly detection on key metrics using simple statistical thresholds, with alerts routed to a Slack channel. I also documented expected data shapes in a lightweight data contract shared with upstream teams.

*Result:* We went from discovering data issues via stakeholder complaints to catching them automatically in the pipeline. The business team reported a clear improvement in their confidence in the dashboards within the first two months.

For technical deep-dives on past work: Use STAR. Spend a small part of your answer setting context (the problem, your role) and most of it on the specific actions you took and what resulted. Avoid 'we did this' without clarifying what your individual contribution was.

For system design and architecture questions: Start by clarifying requirements before jumping to a solution. Ask about scale (how much data per day?), latency (batch overnight or near real-time?), and reliability needs. Then walk through your design layer by layer: ingestion, storage, transformation, serving. Name trade-offs explicitly.

For SQL problems: Think out loud as you write. Interviewers at VIEWS typically care more about your reasoning than about getting perfect syntax on the first attempt. If you are unsure of a function name, say so and describe what it should do.

For tool or technology questions: If you have not used a specific tool, say so honestly. Then describe how you would evaluate it: what properties matter (latency, fault tolerance, community support), and which tool you have used that solves a similar problem. This shows structured thinking and intellectual honesty, both valued traits.

For behavioural questions: Anchor answers in concrete situations. Avoid generic statements like 'I am a good communicator.' Instead, describe a specific moment where clear communication made a measurable difference to an outcome.

Based on candidate-reported experiences, VIEWS Data Engineer interviewers focus on a few consistent themes.

Hands-on pipeline ownership. They want to hear about real pipelines you have built and maintained, not just tools you have listed on a resume. Be ready to go deep on at least one end-to-end pipeline: what it does, what you built, what broke, and what you did about it.

Data quality mindset. Candidates who proactively mention testing, monitoring, and alerting in their pipeline descriptions consistently stand out. Treating data quality as an afterthought is a common gap interviewers notice and flag.

Cloud data platform familiarity. Comfort with at least one cloud ecosystem (AWS, GCP, or Azure) is expected. You should be able to discuss the relevant storage, compute, and orchestration services and when you would choose each.

Structured problem-solving. For design questions, interviewers look for candidates who ask clarifying questions before proposing solutions. Jumping straight to an answer without understanding requirements signals that you skip this step in real work too.

Clear communication. Data Engineers at VIEWS work closely with analytics, product, and business teams. Being able to explain a technical decision in plain language, without jargon, is as important as the technical correctness of the answer itself.

Week 1: Core technical skills

Revise SQL window functions, joins, group by patterns, and query optimization. Practice writing queries from scratch, not just reading them. For Python, focus on working with dataframes (pandas or PySpark), file I/O, and writing clean transformation logic. Getting comfortable writing code without an IDE is useful since some assessments are done in a plain editor.

Week 2: Pipelines and architecture

Pick one end-to-end pipeline you have built and prepare to walk through it in detail: the architecture, tool choices, how failures are handled, and what data quality checks are in place. Revisit batch vs. stream trade-offs and refresh your knowledge of at least one orchestration tool. If applying for a senior or lead role, practice designing a scalable data platform from a prompt, starting with requirements clarification.

Week 3: Mock interviews and STAR stories

Prepare four to five STAR stories covering: a pipeline you built end to end, a production incident you resolved, a data quality improvement you drove, and a time you worked across teams to deliver something. Run at least two mock technical interviews with a peer or on a practice platform, and use the feedback to tighten your answers.

Day before the interview

Read any publicly available information about VIEWS: their product, engineering blog posts, or LinkedIn content. Prepare two or three questions to ask your interviewer about the team's data stack or roadmap. Confirm the interview format with your recruiter so there are no surprises on the day.

Memorizing answers without understanding the reasoning. Interviewers follow up on every answer. If you cannot explain why you made a particular technical choice, it becomes apparent very quickly. Focus on understanding concepts, not scripting responses.

Leaving data quality out of pipeline descriptions. Saying 'I built a pipeline that ingests X and transforms Y' without mentioning how you handle bad data, schema changes, or failures is a pattern interviewers flag as a significant gap.

Overclaiming tool experience. If you have only touched a tool in a tutorial or small side project, say so. Interviewers probe depth, and overclaiming leads to uncomfortable follow-ups that undermine your credibility on everything else you say.

Skipping requirements on design questions. Jumping into a system design answer without clarifying scale, latency, and reliability requirements signals that you skip this step in real work too. Always ask clarifying questions first.

Vague impact statements. Results like 'it worked well' or 'the team was happy' do not land. Tie your work to a business outcome: faster reports, fewer production incidents, reduced manual effort. Even directional statements ('we cut the time from two days to overnight') are far stronger than nothing.

Not preparing questions to ask. Interviewers notice when candidates have nothing to ask at the end. Questions about the team's data stack, biggest engineering challenges, or roadmap priorities show genuine interest and help you assess whether the role is actually right for you.

If you are actively applying to VIEWS and similar companies at the same time, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR for you, so you are not missing openings while you are busy preparing.