All Generation Tech Data Engineer Interview: Questions, Experience & Prep (2026)

All Generation Tech currently has 20 open Data Engineer roles, making it one of the more active hirers in this space right now. Across India, 542 Data Engineer positions are live as of July 2026, with Bangalore leading at 92 openings and Delhi close behind at 66.

Candidates report a process that typically runs through a recruiter screening, one or more technical rounds covering SQL, Python, and data architecture, and a final round focused on behavioral questions or a conversation with the hiring manager. Expect the technical bar to be genuine: interviewers typically ask you to write real queries and design real systems.

Salary bands for Data Engineers in India run from 6-12 LPA at entry level (0-2 years) to 42-65+ LPA for Lead/Staff roles. Mid-level engineers (3-5 years) typically land in the 14-26 LPA range, and senior engineers (6-9 years) in the 28-45 LPA band.

Based on candidate reports, these questions come up most often in All Generation Tech Data Engineer interviews. Prepare a concrete answer for each one before your first round.

1. Walk me through how you would design an end-to-end data pipeline for ingesting and transforming large volumes of raw event data.
2. How do you handle schema evolution in a data warehouse? What happens when upstream teams change the structure of their data?
3. Explain the difference between a data lake and a data warehouse. When would you recommend each?
4. You have a pipeline that runs nightly and has started failing intermittently. Walk me through your debugging approach.
5. How do you ensure data quality in your pipelines? What checks do you build in, and how do you handle failures?
6. Describe a time when you had to optimize a slow SQL query. What was the problem and how did you fix it?
7. How do you approach partitioning and indexing in a large-scale database? What tradeoffs do you consider?
8. What is your experience with streaming platforms like Kafka or Kinesis? How do they compare to batch processing?
9. Tell me about a data project you led from requirements gathering to delivery. What was the hardest part?
10. How do you prioritize when multiple stakeholders are asking for pipelines at the same time?
11. What cloud data services have you worked with (BigQuery, Redshift, Snowflake, Databricks)? How do you choose between them?
12. How do you document your pipelines and ensure other engineers can maintain them after you leave?

Q: Walk me through how you would design an end-to-end data pipeline for ingesting and transforming large volumes of raw event data.

*Situation:* At my previous company, our mobile app was generating millions of user events daily, but the data team had no reliable way to ingest and analyze this data in near-real time.

*Task:* I was asked to design and build a scalable ingestion pipeline that could handle traffic spikes without losing data.

*Action:* I set up a Kafka topic to buffer incoming events, wrote a Python consumer that validated and transformed the data, then loaded it into a partitioned BigQuery table. I added a dead-letter queue for malformed records and set up alerting on consumer lag and error rates.

*Result:* The pipeline handled peak traffic without drops. The analytics team could query data that was near-real time, and we caught and fixed three data quality issues in the first week.

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Q: Describe a time when you had to optimize a slow SQL query.

*Situation:* A dashboard used by the business team was timing out because a core query was doing a full table scan on a fact table with hundreds of millions of rows.

*Task:* I needed to bring query time down dramatically without changing what the dashboard showed.

*Action:* I ran EXPLAIN ANALYZE to find the bottleneck, added a composite index on the most-filtered columns, rewrote a correlated subquery as a CTE, and moved a date filter earlier in the execution plan so it could reduce the row count before the expensive join.

*Result:* Query time dropped from over two minutes to under five seconds. The business team stopped seeing timeout errors, and the fix required no changes to the dashboard itself.

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Q: Tell me about a data project you led from requirements gathering to delivery.

*Situation:* Our sales team wanted a weekly report on pipeline health by region, but the underlying data lived in three different source systems that had never been joined together.

*Task:* I was the sole data engineer assigned, so I owned the full scope from requirements to launch.

*Action:* I ran discovery sessions with the sales ops lead to pin down exactly which metrics they needed. I built a staging layer to clean each source, then a mart layer that joined them on customer ID. I scheduled the refresh for every Sunday night and set up a Slack alert if it failed.

*Result:* The report went live and the sales team adopted it as their main planning tool within a month. Two other teams later built their own reports on the same mart.

Use STAR for every behavioral question. Situation sets the scene briefly (one or two sentences), Task explains what you were responsible for, Action is where you spend most of your time covering the specific steps you personally took, and Result is a concrete outcome. Quantify where you honestly can, but never invent numbers.

For technical design questions, use this structure: scope, design, tradeoffs. Start by clarifying scale and constraints ('how much data, how fresh does it need to be?'), then sketch the components and how they connect, then call out the tradeoffs you considered. Interviewers care more about your reasoning than about landing on a single correct answer.

For debugging questions, walk through your process out loud: what you would check first, how you would narrow down the problem, and how you would confirm the fix. A methodical approach matters more than immediately naming the right answer.

For 'tell me about yourself', keep it short. Cover your current role, one or two highlights directly relevant to data engineering, and why you are interested in this specific role at All Generation Tech.

Candidates report that All Generation Tech interviewers place a high premium on ownership. They want to see that you built things end-to-end, not just contributed pieces. Be ready to talk about the full lifecycle of a pipeline you have owned, including how you handled failures and kept it running over time.

Depth over breadth on SQL and Python. Expect to write actual queries and code, not just describe concepts. Practice window functions, CTEs, and query optimization. On the Python side, be comfortable with pandas, PySpark, or whichever framework the job description highlights.

System design thinking. Even for mid-level roles, candidates report being asked to design data architectures from scratch. Know the tradeoffs between batch and streaming, data lakes vs. warehouses, and how to handle schema changes gracefully.

Communication with non-technical stakeholders. Data engineers at most companies work closely with analysts and business teams. Interviewers typically look for evidence that you can translate technical constraints into plain language and push back constructively when requirements are vague.

Week 1: Technical foundations

1. Spend several focused sessions writing SQL from scratch: window functions, CTEs, GROUP BY edge cases, and query optimization using EXPLAIN. Do not just read about these, write them.
2. Pick one Python data library (pandas or PySpark) and solve at least five end-to-end data transformation problems working with messy, real-looking data.
3. Review the core concepts of one cloud data platform relevant to the role (BigQuery, Redshift, or Snowflake). Read the official docs on partitioning and clustering.
4. Study data pipeline architecture patterns: batch vs. streaming, ELT vs. ETL, and orchestration tools like Airflow or Prefect.

Week 2: Company and interview prep

1. Research All Generation Tech: check their engineering blog if one exists, look at LinkedIn to understand the team's tech stack, and read the job description carefully to identify the specific tools they use.
2. Prepare three to four STAR stories from your own experience: a complex pipeline you built, a data quality incident you resolved, and a time you navigated a difficult or unclear stakeholder request.
3. Do at least two mock interviews out loud, not just in your head. Time your answers and cut anything that drags on too long.
4. Review every bullet on your resume and be ready to go deep on each one. Interviewers often pick a project from your resume and probe it in depth.

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Vague answers about past work. Saying 'I worked on a data platform' without specifics signals that you may not have owned the work. Practice naming the exact tools, the scale, and the specific problem you solved.

Skipping clarifying questions in design rounds. Jumping straight into an architecture without asking about scale, latency requirements, or budget signals poor engineering judgment. Always clarify before you design.

Over-claiming on tools. Candidates who list many tools on their resume and cannot go deep on any of them lose credibility quickly. Know two or three tools very well rather than knowing many superficially.

Ignoring data quality. Many candidates design pipelines that handle the happy path but have no answer for what happens when data arrives late, malformed, or duplicated. Always address error handling and monitoring.

Underestimating behavioral rounds. Data engineers sometimes over-prepare technical content and walk into behavioral rounds with no concrete stories ready. 'I am a good communicator' is not a story. Prepare specific examples.

Not asking questions at the end. Candidates with no questions signal low interest. Prepare two or three genuine questions about the team's current data stack, how quality issues are handled today, or what the ramp-up period looks like.