Apple Data Engineer candidates report a hiring-manager screen followed by a varied five-round loop centered on SQL, Python, past work, and data-engineering fundamentals.
$199K
Avg. Base Comp
$272K
Avg. Total Comp
5 rounds
Typical Rounds
2-4 weeks
Process Length
Apple Data Engineer candidates describe a hiring-manager conversation that combines discussion of prior work with a short SQL exercise, followed by a varied loop. Across the reports, SQL and Python are the clearest recurring themes: candidates encountered query-review tasks such as spotting a missing join condition or aggregation issue, coding questions, and follow-ups that moved between Python and Java.
Preparation should therefore include reading unfamiliar SQL carefully, explaining why a query is wrong before proposing a correction, and writing clear Python under time pressure. One candidate also reported conceptual discussion of deduplicating streaming data, plus database design and storage fundamentals. Be ready to describe how you would reason about duplicate records and data consistency, rather than treating every prompt as a pure coding exercise.
A project-focused discussion also appears in the reports. Practice walking through one or two projects: the problem, your design choices, trade-offs, and how you know the solution worked. Candidates reported behavioral content alongside technical questions, so concise explanations of past work can help connect technical decisions to impact. The available reports are limited, but they consistently point to an interview that can switch quickly among SQL, programming, and project reasoning.
Synthesized from 3 candidate reports by our editorial team.
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| 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 | |
| Hurdles In Data Projects | |
| Xgboost vs Random Forest | |
| Reducing Error Margin | |
| Swiping App Design | |
| Real-Time Hashtag Partitioning | |
| Transformer Encoder Layer | |
| Nearest Common Ancestor | |
| Walking Robot |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial hiring-manager conversation that included discussion of previous work and a brief technical question. One candidate described roughly 20 minutes on past work and 20 minutes identifying issues across SQL queries, including a missing join condition and an aggregation problem.
One candidate explicitly described a five-round full loop, while another described a hiring-manager screen followed by four additional rounds. Reports characterize the loop as varied and fast-paced, with technical and behavioral material; the precise structure may differ by team.
Candidates report SQL, Python, and PL/SQL questions. Examples include diagnosing SQL query issues and an easy LeetCode-style programming problem. One report says an answer initially written in Python was followed by a request to write in Java.
Candidates report conceptual questions about deduplicating streamed data as well as database-design and storage topics. Prepare to explain the reasoning behind an approach, including how it addresses duplicate records and data consistency.
Candidates report being asked to walk through one or two projects, explain design decisions, and answer follow-up conceptual questions. A clear account of the problem, choices, and trade-offs may be as important as naming the tools used.
