Impetus Data Engineer candidates describe a short loop of technical rounds on SQL, Spark, Python and cloud tooling, plus an HR conversation. Reports cover GCP, Spark internals and project deep dives.
$108K
Avg. Base Comp
$130K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Across three candidate reports, Impetus data engineer interviews lean on SQL and Spark fundamentals and on how well you can explain work you have actually done. The depth varies by interviewer and stack, so prepare the core and expect it to be probed.
Two candidates describe a loop of two technical rounds and an HR round. One of them says the first technical round paired SQL joins and group by with the same logic rewritten in PySpark, along with precise questions on cache versus persist, repartition versus coalesce, and RDDs, DataFrames and Datasets. Their second round added union versus union all, Spark architecture and Hive questions tied to past projects.
The other candidate, interviewing for a GCP-focused role, reports a first round covering Python, SQL and GCP services, with PySpark also coming up. Their second technical round centered on a detailed walkthrough of their own project: what was built and why, followed by intermediate Python coding questions.
A third candidate reports a more advanced Spark conversation with two interviewers on Microsoft Teams. Topics included driver-executor memory, DAG execution, Catalyst and adaptive query execution, skewed data, broadcast join defaults, a third-highest-salary SQL problem, and Databricks with Unity Catalog.
Practically, rehearse SQL join and aggregation problems, write PySpark by hand, and be ready to defend design choices in a project you know well. One candidate cleared every round but received no offer and was told it was due to budget and approval constraints, so it is reasonable to ask about headcount status early.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Impetus process.
I interviewed at Impetus for a Data Engineer role, and the process was surprisingly hands-on and focused on practical skills over theory. The recruiter called me to schedule a technical round, which was conducted virtually on Microsoft Teams with a panel of two interviewers. They were straightforward and direct — not interested in textbook answers, but rather in whether I'd actually worked with these tools in production.
The first round dug deep into Spark fundamentals and I quickly realized they wanted me to demonstrate real understanding, not just memorization. We covered advanced Spark internals including driver-executor memory management, task scheduling and DAG execution flow. The conversation moved into execution plans — both logical and physical — and the Catalyst Optimizer, with specific examples of how adaptive query execution worked. They asked about structured streaming, dynamic resource allocation, and handling skewed data. We also discussed job failure and retry mechanisms, scheduler types, and the nuances of join operations, including when to change broadcast join defaults and why. Then there were deeper dives into SQL optimization and specific coding problems like finding the third highest salary. They touched on Databricks and Azure Databricks architecture, plus Unity Catalog, showing they care about the modern data stack. The technical questions weren't abstract — they were grounded in real problems you'd solve in a data pipeline. Throughout, the HR team was genuinely helpful and the overall process moved quickly. I appreciated that they valued hands-on experience and didn't waste time on purely theoretical knowledge I could look up. The panel seemed interested in whether I understood the 'why' behind optimization choices and architectural decisions.
Prep tip from this candidate
Focus heavily on Spark internals — driver-executor memory, DAG execution, and Catalyst/AQE optimization are non-negotiable. Be ready with concrete examples of when you'd use broadcast joins versus sort-merge joins, and prepare practical SQL problems like window functions and self-joins. They prioritize hands-on experience, so talk through real production scenarios you've debugged rather than theoretical concepts.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Impetus
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Largest Salary by Department | |
| Words in Encrypted String | |
| String Shift | |
| Hurdles In Data Projects | |
| Random Forest Explanation | |
| Skyscanner Partner ETL | |
| Data Preparation for Imbalanced Data | |
| Xgboost vs Random Forest | |
| Duplicate Rows | |
| RAG Strict Source Control | |
| Data Pipelines and Aggregation | |
| Assumptions of Linear Regression | |
| Scalable Data Pipelines | |
| Why Do You Want to Work With Us | |
| Payment Data Pipeline | |
| Ranking Metrics | |
| Your Strengths and Weaknesses | |
| Unstructured Data Pipeline (ETL) | |
| SFTP Pipeline | |
| Evaluating Revenue Decline | |
| Account Personalization Strategy | |
| Text Search System | |
| Using APIs for Downstream Tasks | |
| Effectiveness of Sales | |
| Correlation in Regression | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Closest SAT Scores |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports the process began with an HR call that walked through the steps and asked for current and expected CTC. Another says a recruiter called to schedule the first technical round. Expect logistics and compensation questions early, though this may vary by team.
Candidates report fundamentals-heavy questions. One describes SQL joins and group by, rewritten in PySpark, plus cache versus persist and repartition versus coalesce. Another describes Python, SQL and GCP services. A third reports a Teams panel on Spark internals, execution plans and a third-highest-salary query.
Two candidates describe a second technical round. One reports union versus union all, Spark architecture and Hive questions tied to past projects. The other reports detailed questions on the project they built, followed by intermediate Python coding. Prepare to explain your own design choices out loud.
Two candidates report a final HR conversation described as friendly and straightforward. One says it covered an introduction, what they knew about Impetus, why they were changing jobs and a CTC breakdown. That candidate cleared every round but received no offer because of budget constraints, so it may help to ask about hiring status.
