Flowhub Data Engineer candidates report a fast SQL-and-Python screen followed, in one account, by interviews on product sense, data modeling, ownership, and collaboration. Prepare for volume, clear reasoning, and business-schema queries.
$125K
Avg. Base Comp
$145K
Avg. Total Comp
2-5 rounds
Typical Rounds
Not reported
Process Length
Flowhub Data Engineer candidates most consistently describe a hiring process built around speed across SQL and Python, rather than a single deeply algorithmic problem. Recruiter contact appears first in several accounts: it may be a resume-focused conversation or a brisk oral check of data modeling, data structures, and SQL concepts. One reported conceptual prompt compared UNION with UNION ALL.
The technical screen is the clearest shared signal. Candidates report roughly 50–60 minutes split between SQL and Python, often with the choice of which section to start first. Accounts describe five questions per section or a target of about three correct solutions in each area. SQL prompts use a shared bookstore-style business schema and include joins, aggregations, percentages, CTEs, and window functions. Python is described as practical collection work with arrays, lists, dictionaries, sets, tuples, heaps, and hash maps; the task is to read a business scenario quickly and produce working code while explaining it.
One candidate who advanced beyond the screen reported a three-interview loop covering product sense, data modeling, SQL, Python, and ownership. Expect to discuss how data should be structured and how technical choices connect to a product, along with collaboration, independence, scope, and responsibility. Practice concise explanations of your choices as you work, since follow-ups may test the reasoning behind a solution. Available accounts provide limited detail on the complete process for every candidate.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Flowhub process.
The hardest part of the technical interview was not the complexity of the questions but the clock. I first had a phone call with a recruiter who was very friendly and made moving forward feel almost like a foregone conclusion. It was a straightforward opening conversation, without any unusual screening format.
The next round was a live paired-programming assessment covering six problems in total: three Python questions and three SQL questions. The individual problems were not especially difficult, but completing that many during one timed session made the round challenging. The questions felt similar to the medium-level exercises on the company’s practice site and to LeetCode medium problems. The broader process was fairly textbook and focused on the same core areas throughout: SQL, relatively easy Python, and product-metrics thinking. Later-stage interviews followed a consistent format rather than introducing a radically different type of exercise.
I ultimately did not receive an offer. My main takeaway is that getting the questions right may not be enough, so I would prepare specifically for speed and clean execution. Practice switching quickly between Python and SQL under a strict time limit, and make sure you can explain your reasoning while coding rather than treating the session like a silent take-home test.
Prep tip from this candidate
Simulate the paired-programming round by completing three medium-level Python problems and three SQL problems in one timed session. Also review product-metrics questions, since that topic appeared alongside the SQL and Python evaluation.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Flowhub
Write a query that returns all neighborhoods that have 0 users.
| Question | |
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| 2nd Highest Salary | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Experiment Validity | |
| Subscription Overlap | |
| Download Facts | |
| Top 3 Users | |
| Customer Orders | |
| String Shift | |
| Average Quantity | |
| Last Transaction | |
| Random SQL Sample | |
| Manager Team Sizes | |
| Size of Joins | |
| Month Over Month | |
| Flight Records | |
| Prime to N | |
| Find the First Non-Repeating Character in a String | |
| Paired Products | |
| Upsell Transactions | |
| Monthly Customer Report | |
| RMS Error | |
| First Touch Attribution | |
| Daily Retention Summary | |
| Recurring Character | |
| Compute Deviation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an opening recruiter call that may focus on prior data-engineering experience and role fit. One account instead described a quick series of short questions on SQL, data modeling, and basic data structures, so prepare concise explanations as well as your resume narrative.
Candidates report a split coding assessment with SQL and Python sections, sometimes five prompts per section and a benchmark of roughly three correct answers in each. You may choose the starting section, but the repeated challenge is pacing rather than unusually advanced algorithms.
Reported SQL questions use a bookstore-style schema with related records such as authors, transactions, customers, payments, copies, and renewals. Practice deriving answers from table definitions without sample data, including joins, grouped counts, percentages, CTEs, and window functions.
One candidate who progressed beyond the screen reported three later interviews covering product sense, data modeling, SQL, Python, and ownership. Prepare to connect a data-modeling decision to product needs and discuss collaboration, independence, scope, and responsibility; the exact loop may vary.
