Mastercard Data Engineer interviews emphasize SQL, Spark or big-data architecture, and estimation/business-case questions, with reported structures of roughly three to four rounds across five candidate accounts, one of which ended in an offer.
$128K
Avg. Base Comp
$148K
Avg. Total Comp
3-4 rounds
Typical Rounds
Not reported
Process Length
Mastercard's Data Engineer interviews combine SQL depth, big-data systems concepts, and estimation-style problem solving, based on five candidate reports. The accounts describe different paths through the process — a four-round pipeline ending in an onsite, a three-round virtual onsite sequence for a Data Engineer II req, a two-round-plus-HR structure after an online assessment, and a three-round loop with a hiring manager finish that produced an offer — so structure and round count visibly vary by req and interviewer rather than following one fixed template.
What stays consistent is the subject matter. SQL shows up in nearly every account, with candidates specifically flagging window functions, stored procedures, and query complexity that escalates as the round progresses. Several reports also describe conceptual deep-dives rather than pure coding: one candidate was pushed on data warehousing history and ACID behavior in Delta tables, while another spent real time explaining Spark's worker/slave model and how jobs get distributed across a cluster. Estimation and puzzle-style questions appear independently in three accounts — guesstimates, a market-sizing question about students choosing a grade level, and a business case about Starbucks switching to iPad ordering — suggesting interviewers use these to see how candidates structure ambiguous problems.
Several candidates also described slow or absent feedback after final rounds, and one reported a role being reposted after rejection, so pair your technical prep with realistic expectations on timeline and follow-up.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Mastercard process.
The online assessment was straightforward and covered the full range of topics—quantitative reasoning, data interpretation, and verbal skills—with a good balance across all sections. The interview process itself had two substantive rounds plus an HR conversation at the end. In the first round, the interviewer walked through my resume and projects, then moved into puzzles and estimation questions. They asked me to estimate the number of students choosing 12th grade at private colleges in a metropolitan city, which required breaking down the problem into reasonable assumptions about regional demographics and education preferences. After that came some advanced SQL queries, which tested both syntax knowledge and ability to think through complex data transformations. The second round was lighter—mostly basic math and another round of puzzles and estimation problems. I noticed the interviewers seemed less prepared than I'd have hoped; they didn't dig deeply into my background and sometimes appeared to be reading my resume for the first time during the call. The HR round at the end was pretty casual, focused mainly on logistics like location, the role itself, and compensation structure. Overall, the process felt somewhat disorganized from a preparation standpoint, though the questions themselves were reasonable.
Prep tip from this candidate
Focus heavily on estimation and puzzle-solving skills—those came up repeatedly and seemed to be a core part of how they evaluate candidates. Make sure your SQL is sharp on window functions and complex queries, not just basic joins, since that was explicitly tested in the technical round.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Mastercard
Select the 2nd highest salary in the engineering department
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report two different entry points: one account describes an online assessment covering quantitative reasoning, data interpretation, and verbal sections, while another describes a recruiter phone screen focused on ETL fundamentals and data transformation basics. A third candidate was pushed through an unusually fast recruiter-driven schedule, with multiple interviews booked back-to-back within a day of first contact, so scheduling pace may vary.
SQL appears in nearly every candidate account, often live-coded and ramping in difficulty. Candidates mention window functions, stored procedures, ETL-related query logic, and general query design, with one noting questions escalated in complexity as the round progressed. Some rounds pair SQL with lighter coding tasks like string manipulation or API-related problems.
Several candidates report a round dedicated to data systems concepts rather than pure coding. One was asked to explain Spark's worker/slave model and how jobs distribute across a cluster; another worked through a data modeling design problem with a senior engineer; a third fielded detailed questions on data warehousing history and ACID guarantees in Delta tables, plus a system-design prompt around a dashboard that scales with real-time refresh.
Estimation-style questions show up independently across three accounts. Examples reported include a guesstimate on the number of students choosing a grade level at private colleges in a metro area, a business case about how a company's bidding strategy might change if Starbucks switched to iPad ordering, and general guesstimate/aptitude problems paired with coding. Practicing structured assumption-building helps with these prompts.
Final or mid-process rounds typically include a behavioral component, sometimes with a VP or hiring manager. One candidate reports 20-25 minutes of behavioral questions before a business case; another describes a final hiring-manager round focused on background, contribution, and what the candidate wants from the role, with less technical depth than earlier rounds.
One candidate describes a casual HR conversation at the end covering location, role scope, and compensation structure. Others report slow or absent follow-up: one candidate's promised feedback never materialized and their calls and emails went unanswered, while another heard nothing for weeks before a rejection and then saw the role reposted, so set expectations on follow-up timing.
