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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| Assumptions of Linear Regression | |
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| Why Do You Want to Work With Us | |
| Payment Data Pipeline | |
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| SFTP Pipeline | |
| Evaluating Revenue Decline | |
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| Using APIs for Downstream Tasks | |
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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.
