Apple Data Engineer interviews reported by candidates start with a hiring manager conversation, then a technical screen and a loop heavy on SQL, Python, pipeline design and project deep-dives.
$172K
Avg. Base Comp
$230K
Avg. Total Comp
5-7 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the Apple Data Engineer process as SQL- and Python-heavy, with design and project discussion layered on top. Several report starting with a hiring manager conversation, and loop lengths range from about five rounds to six or seven depending on the team.
SQL comes up most often. One candidate was shown roughly five short queries and asked to spot the bug in each, such as a join with no ON condition and a count with no GROUP BY. Others report window functions, aggregations, pivots, multi-table joins and deduplication. Python questions are described as easy to medium, including word-frequency counting, tree traversal and an easy LeetCode-style problem. Two candidates were then asked how they would scale a solution, one for thousands of pages and one for a median-finding approach on large lists.
Pipeline and storage thinking appears across reports. Candidates mention deduplicating streamed data, database design, Parquet versus CSV, Spark versus MapReduce, and Spark or AWS architecture. A candidate on the App Store data team described a system design round with the hiring manager and a Scala data manipulation round, and said the loop felt closer to software engineering than data engineering.
Hiring managers also probe your own work. Expect to walk through a project, explain what you owned and what changed for the business, and defend your design choices. One candidate said communication with non-technical stakeholders got real time. Another said the recruiter gave no preview of the loop, so prepare broadly across SQL, coding, design and behavioral questions. Outcomes in these reports were mostly rejections, and time pressure was a recurring complaint.
Synthesized from 11 candidate reports by our editorial team.
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Real interview reports from people who went through the Apple process.
My interview process at Apple for a Data Engineer role spanned several months and involved multiple technical rounds. It started with a phone screen to assess fit, followed by a technical screen where I was asked general questions about what data metrics I'd collect in specific scenarios. This felt like a warm-up to gauge my thinking around instrumentation and business impact rather than pure coding.
The full onsite came later and consisted of four to five rounds. What surprised me was the breadth of what they tested. I encountered a mix of coding problems—tree and array problems at medium complexity—alongside hands-on technical questions about infrastructure decisions. They'd ask scenario-based questions like how you'd join large datasets or work with formats like Parquet, then dig deeper based on your background. One round focused on SQL and data structure fundamentals, while another went into Spark and AWS architecture questions, exploring how you'd design systems at scale. The team was friendly and welcoming when I arrived, which made the full day feel collaborative rather than adversarial.
What I wish I'd known going in was that the timeline can be unpredictable. The entire process took me about six months from first phone screen to final rounds, with significant gaps in communication between stages. That's worth bracing yourself for mentally. The technical bar is solid but not extreme—they're testing whether you can think through distributed systems trade-offs, write decent code under time pressure, and reason about real infrastructure problems you'd actually face on the job. Come prepared to discuss your past work in detail because they'll use that as a springboard into deeper technical conversations.
Prep tip from this candidate
Focus on Spark, AWS architecture, and SQL window functions alongside medium-level coding problems. Be ready for scenario-based questions about joining large datasets and data format choices like Parquet—those tend to come up across multiple rounds and let you demonstrate systems thinking.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Apple
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Experiment Validity | |
| Random SQL Sample | |
| Prime to N | |
| Upsell Transactions | |
| Paired Products | |
| Find the Missing Number | |
| Recurring Character | |
| Retailer Data Warehouse | |
| Cumulative Sales Since Last Restocking | |
| Completed Shipments | |
| Detecting ECG Tachycardia Runs | |
| Size of Joins | |
| The Brackets Problem | |
| Google Maps Improvement | |
| Groups of Anagrams | |
| Random Forest Explanation | |
| Cyclic Detection | |
| Exam Scores | |
| Radix Addition | |
| Equivalent Index | |
| Bucket Test Scores | |
| Xgboost vs Random Forest | |
| Hurdles In Data Projects | |
| Reducing Error Margin | |
| Real-Time Hashtag Partitioning | |
| Swiping App Design | |
| Transformer Encoder Layer | |
| Nearest Common Ancestor | |
| RAG Strict Source Control |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report opening with a recruiter or a hiring manager call of about 45 minutes. One person discussed past work for about 20 minutes, then found issues in SQL queries. Others were asked to describe a complex data project, what they owned and the business impact. Have one project story ready that you can take apart at each level.
Candidates describe a technical screen with easy to medium SQL and Python. Reported tasks include word-frequency counting with a follow-up on scaling to thousands of pages, and deduplicating streamed data. One candidate with a longer screen also reported ETL, window functions and tool comparisons. Expect to explain your reasoning as well as write correct code.
Candidates report loop rounds with SQL joins, window functions and aggregations alongside Python or other languages. One was asked to rewrite a Python answer in Java, and another saw an LRU Cache variant. Some described an online editor with the prompt on one side and a runnable editor on the other. Several said the pace was fast, so practice timed problems.
Several candidates report design-oriented rounds covering scalable pipelines, schema or data modeling, query optimization and storage choices. One App Store team candidate had a pipeline design round with the hiring manager. Another described Spark and AWS architecture questions. Be ready to justify trade-offs between tools rather than recite definitions.
Candidates report conversations that revisit their résumé, such as explaining design decisions and conceptual follow-ups, plus behavioral topics like missed deadlines and long projects. A senior engineer chat also appears in one report. Practice explaining technical choices clearly and why you want to work at Apple.
