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Most people prepping for data engineer interview questions do it the same way they’d prep for a general software engineering interview: grind LeetCode, drill dynamic programming, practice graph traversal. Real candidate reports from Google, Amazon, Meta and dozens of other companies say that’s the wrong emphasis. One aggregator site, drawing on a claimed 2,817 verified interview reports across 920 companies, puts it bluntly: for most data engineer loops, “your algorithm prep is over-leveled.”
What actually decides these interviews is SQL, Python for data manipulation, data modeling, and pipeline or system design — with dynamic programming and graph puzzles showing up rarely, if at all. This guide breaks down what candidates actually report being asked, round by round and company by company, so you spend your limited prep time where it counts.
The Algorithm Mistake Behind Data Engineer Interview Questions
Search “data engineer interview questions” and you’ll find plenty of generic listicles reusing the same LeetCode-medium problems recommended for any software engineering role. That advice isn’t wrong for every company, but it misreads what most data engineering loops actually test.
The aggregator site referenced above states the loop is “not algorithm-heavy” and that its dataset shows “almost no dynamic programming or graph puzzles.” Instead, SQL dominates at the screening stage, Python shows up mostly for data manipulation (pandas, parsing semi-structured data, PySpark) rather than algorithmic problem-solving, and data modeling or system design “decide most loops” at the senior level.
This mirrors a pattern we’ve found elsewhere on data-platform-adjacent roles: our Databricks interview questions guide found the same product-trivia-versus-get-hired split, and our cloud engineer interview questions guide found that job titles sharing a two-word label can hide genuinely different interviews. “Data engineer interview questions” is no exception — the title is consistent, but what it actually tests skews heavily toward data skills over general algorithmic ability.
What Candidates Actually Report, Round by Round
Cross-referencing Glassdoor’s aggregated data engineer interview page (over 3,500 candidate reports), company-specific Glassdoor pages for Google and Amazon, and Blind threads on Meta’s process gives a consistent five-round shape.
The SQL Round
SQL is the single most consistently reported component across every source checked. Meta’s phone screen, per a Blind thread, runs “5 Python + 5 SQL questions in 50 mins,” with one candidate noting the difficulty is “fairly simple DS related” rather than algorithmic. Amazon candidates report a phone screen that “covers SQL fundamentals and behavioral questions,” and a July 2026 report describes the round as “SQL question, about previous experience, little bit of data modeling and 1 behavior question.” Glassdoor’s aggregated page names specific recurring SQL topics: GROUP BY, aggregate functions, HAVING clauses, window functions and anti-joins.
The Python Round
Where Python shows up, it’s typically data-manipulation Python — pandas, parsing semi-structured data, PySpark — not algorithmic Python. Google’s process includes a dedicated “Python coding” round alongside “advanced SQL,” and Amazon’s coding round is described as “standard Python implementation” paired with SQL rather than a separate algorithms round.
Data Modeling
This is where multiple sources converge on calling out a real filter round. A Google candidate’s onsite included “data modeling” and “pipeline design” as distinct sessions, covering star schemas, slowly changing dimensions, and table optimization for scale. Amazon’s process names data modeling directly as “the real filter here, you have to design a schema from scratch,” with an August 2026 report describing the prompt as “Design a data model for Amazon Prime.”
System and Pipeline Design
At senior levels, this round carries real weight. Reported Google prompts include “How would you design and build an end-to-end batch data pipeline?” and “Design a data pipeline to process clickstream events from millions of users in real time” — with follow-up discussion on batch versus streaming, idempotency, and late-arriving data. Amazon’s equivalent round is framed around ETL architecture and AWS service selection rather than an abstract system-design exercise.
Behavioral
Every source checked includes a behavioral component, and at Amazon it isn’t a separate round so much as a lens applied throughout: Leadership Principles are “integrated across all rounds,” on top of a dedicated Bar Raiser session, and candidates are advised to prepare five to six STAR-format stories in advance. Google’s version is described more simply as “googleyness” and culture fit, alongside the plain “why do you want to work here” question.
Data Engineer Interview Questions by Company
| Company | Rounds | Algorithm Emphasis | Distinguishing Feature |
|---|---|---|---|
| ~5 onsite: data modeling, pipeline design, Python, advanced SQL, behavioral | Low — no dedicated DSA round reported | “Googleyness” culture-fit round; initial phone screen reported as non-technical | |
| Amazon | OA, phone screen, 5 onsite: data modeling, ETL/AWS architecture, SQL+Python, Bar Raiser, Leadership Principles | Low — OA described as “not that difficult” | Leadership Principles woven into every round, not just one |
| Meta | Phone screen: 5 SQL + 5 Python in 50 min; later loop adds data modeling and architecture | Low — candidates describe questions as “fairly simple DS related,” not algorithmic | Meta’s own applicant portal reportedly provides sample SQL/Python questions |
| General / aggregate (Glassdoor, 3,500+ reports) | Resume discussion, SQL, coding (arrays/hash maps), real-world project walkthrough | Present but limited — “arrays and hash maps,” not DP or graphs | Snowflake and cloud-platform experience increasingly asked about directly |
The pattern holds across all three named companies: SQL and data modeling appear in every single one, while dynamic programming and graph algorithms — the bread and butter of generic software engineering prep — appear in none of the reports checked for this article. That’s a real finding, not a universal rule; the Glassdoor aggregate page does mention “coding rounds: algorithm questions focusing on arrays and hash maps” as part of the broader candidate pool, which spans smaller companies and startups that may lean more SWE-style. Treat the “almost no DP or graphs” pattern as the norm at large, well-documented employers, not an absolute guarantee everywhere.
SQL Is Doing More Work Than People Expect
A Blind thread comparing SQL prevalence across role types states plainly that data engineer interviews carry a “very substantial SQL component,” while general software engineer interviews lean on data structures and algorithms instead, with SQL treated as comparatively minor — one commenter notes that after typical LeetCode prep, “LeetCode’s SQL section will be a cakewalk.” That asymmetry is exactly what the round-by-round breakdown above shows: candidates who prepare like a generalist SWE candidate are optimizing for the wrong skill entirely.
Data Engineer Interview Questions to Practice
These are practice exercises built to match the reported format — not verified real exam questions, since most companies don’t publish their actual bank. Use them to rehearse the pattern that candidate reports actually describe: SQL and data modeling first, algorithms a distant third.
Practice 1: Window Function (SQL)
Given a table of daily sales by store, write a query that returns each store’s sales alongside a 7-day trailing average. This mirrors the window-function and conditional-aggregation style repeatedly named in candidate reports, rather than a join-heavy or subquery-nested puzzle.
Practice 2: Grain-First Data Modeling
You’re asked to design a schema for an e-commerce order system (mirroring the reported “design a data model for Amazon Prime” style prompt). Before drawing any tables, state the grain of your fact table explicitly — one row per order, per line item, or per shipment — since interviewers reportedly probe this decision harder than the schema itself.
Practice 3: Batch Pipeline Design
# Practice: sketch the shape of a batch ingestion job,
# not a fully working system. Interviewers reportedly
# care more about idempotency and late-arriving data
# handling than the specific tool choice.
def process_daily_batch(event_date, source_path, target_table):
"""
1. Read raw events for event_date from source_path.
2. De-duplicate by event_id (idempotency).
3. Handle late-arriving events from prior dates
by re-processing a trailing window, not just event_date.
4. Write to target_table using an upsert, not a blind append.
"""
events = read_events(source_path, event_date)
deduped = deduplicate_by_id(events)
late_arrivals = read_late_events(source_path, event_date, window_days=3)
combined = merge_and_dedupe(deduped, late_arrivals)
upsert(target_table, combined, key="event_id")
Where the Overlap With Snowflake and Databricks Comes From
Several of the reports gathered here mention Snowflake and cloud-warehouse experience directly, which tracks with a broader pattern: data engineer interview questions increasingly overlap with platform-specific interview questions, because so much of the day-to-day job now runs on managed platforms rather than hand-rolled infrastructure. If you’re also interviewing at a specific data-platform company, our Snowflake interview questions guide covers that company’s own panel-round structure in detail, and it’s worth reading alongside this generic breakdown rather than instead of it — the two overlap but aren’t identical.
For a broader view of how “data engineer” fits alongside other technical roles with their own distinct interview shapes, see the interview questions by role hub, which collects this same treatment for SDET, QA engineer, cloud engineer, systems engineer and more.
None of this is officially documented by any single employer in a way that settles the question outright, which is part of why data engineer interview questions get misjudged so often — candidates default to general database and computer-occupation career guidance that doesn’t capture how heavily SQL and modeling are weighted in practice.
FAQ: Data Engineer Interview Questions
Do data engineer interviews really skip algorithm questions?+
Not entirely — Glassdoor’s broader aggregate data shows some companies still include coding rounds built around arrays and hash maps. But across Google, Amazon and Meta specifically, candidate reports describe SQL, Python data manipulation, data modeling and pipeline design as the core rounds, with dynamic programming and graph problems largely absent.
Is SQL or Python more important for a data engineer interview?+
Both show up, but SQL is reported more consistently and weighted more heavily. A Blind thread on the topic describes data engineer interviews as carrying a “very substantial SQL component” compared to general software engineer loops.
What does the data modeling round actually test?+
Candidate reports describe schema-design exercises — building a data model from scratch for a scenario like Amazon Prime or an e-commerce platform — with interviewers probing grain, slowly changing dimensions and scalability rather than syntax.
How many rounds does a typical data engineer interview loop have?+
Reports converge on roughly five onsite rounds at large companies (Google, Amazon), on top of a recruiter screen and technical phone screen, spanning 2 to 5 weeks depending on the aggregator’s dataset.
Does Amazon’s data engineer interview really weight Leadership Principles that heavily?+
Yes — reports describe Leadership Principles as “integrated across all rounds” rather than confined to one behavioral session, on top of a dedicated Bar Raiser round.
Should I still practice any algorithms at all?+
Light coverage of basic data-structure fundamentals (arrays, hash maps) is worth keeping, since some companies’ loops include it. But treat it as a small slice of your prep time, not the center of it — the reported evidence points toward SQL and data modeling as the higher-leverage investment.
The Bottom Line on Data Engineer Interview Questions
If you take one thing from the candidate reports gathered here, make it this: data engineer interview questions are decided by SQL fluency, Python data-manipulation skill, and your ability to reason about schema design and pipeline architecture out loud — not by how many LeetCode hards you’ve solved. Prep accordingly, and treat algorithm grinding as a small supplement rather than the main event.
