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Interviewing

Data Engineer Interview Prep for 2–4 Years of Experience

Six weeks, one round of a mid-level loop each, weighted by what gets candidates rejected.

6 weeks94 steps~23h total
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It opens on your dashboard and ticks itself off as you go. Every step below is a chapter or an exercise that already ships.

01

When you finish, you can

You can pass the SQL, Python, modeling, design, Spark and behavioral rounds at mid-level.

Loop day Ready for a mid-level loop

02

The plan, 6 weeks

Ordered by what each one buys you, not by topic. Every step says why it is here.

  1. Week 01 · ~3h

    The SQL screen: correct, then fast

    You can write joins, windows and dedupes correctly against a clock.

    Skip this if you sessionize and rank top-N per group unaided in 20 minutes.

    1. SectionClarify, contract, build, verify, explain7 minThe loop you are graded on, before any syntax.
    2. SectionFan-out: when a join doubles your metric7 minThe most-asked SQL trap, at every level.
    3. InterviewDISTINCT vs GROUP BY6 minFan-out asked as a definition. DISTINCT usually hides it.
    4. ExerciseTop buyers by revenue15 minin the studioAggregate at the right grain first, then rank.
    5. SectionTop N per group6 minOn the screen in almost every loop.
    6. ExerciseTop two tracks in every genre20 minin the studioTop N per group, where the ties matter.
    7. SectionROW_NUMBER for deduplication6 minName the dedupe key before you write the window.
    8. ExerciseDeduplicate order snapshots15 minin the studioLatest row per key: the most reused pattern.
    9. ExerciseRunning daily revenue15 minin the studioRunning totals. The frame clause is the test.
    10. SectionGaps and islands: date minus row number8 minStreaks and runs. Learn the trick once.
    11. ExerciseSessionise plays with a 30-minute rule30 minin the studioSessions from a 30-minute gap: the classic mid-level ask.
    12. InterviewDay 1 / 7 / 30 retention6 minThe definition is the hard part. Say which one.
    13. InterviewIN vs EXISTS6 minThe opener most screens use. NULLs decide it.
    14. SectionEdge-case checklist6 minNULLs, ties, empty groups. Rejection notes name these.
    15. ExerciseInterview top buyers with ties18 minin the studioStretch: the same pattern at interview grade.
    16. QuestionsSQL · Query formulation15 minSay the output contract before you write anything.

  2. Week 02 · ~6h

    Python: the data-wrangling round

    You can parse, group, dedupe and join records in plain Python.

    Skip this if you write top-N per key from raw JSON unaided.

    1. SectionGrouping records by key6 minMost data engineering coding rounds are dicts, not algorithms.
    2. SectionJSON payloads, one document at a time6 minMessy JSON is the usual phone-screen input.
    3. ExerciseFlatten nested event payloads30 minin the studioNested payloads to flat rows, keys intact.
    4. ExerciseSessionize an unsorted event feed35 minin the studioLast week in SQL, now without SQL.
    5. SectionDedupe by business key6 minSay which record wins before you write code.
    6. ExerciseBuild a golden record from several sources40 minin the studioDuplicates that disagree. Pick a winner and defend it.
    7. ExerciseTop N per key, in bounded memory35 minin the studioTop N per key with a heap, not a full sort.
    8. ExerciseLongest browsing run with no repeated page30 minin the studioThe sliding window, as an algorithms round asks it.
    9. ExerciseCount ledger runs that net to a target30 minin the studioPrefix sums in a dict. Reported at Paytm for data engineers.
    10. SectionIn-memory joins with lookup dictionaries6 minIndex once, join many. They check the complexity.
    11. ExerciseJoin two feeds and measure the fan-out35 minin the studioTwo feeds, one key, and the rows that do not match.
    12. ExerciseRetry under a policy, as a decorator35 minin the studioRetries come up whenever an API is involved.
    13. InterviewA 200 GB file on a 16 GB box6 minStreaming versus loading, asked as a design question.
    14. InterviewMoney without floats6 minA correctness trap that sinks otherwise clean code.
    15. InterviewThe list that remembers6 minA classic output-prediction trap. Explain it in one breath.
    16. InterviewMutable vs immutable6 minThe conceptual opener behind half the bugs above.
    17. QuestionsPython · Approach & data structures15 minName the data structure before you write a loop.

  3. Week 03 · ~4h

    Data modeling: the round most people fail

    You can declare the grain and defend a star schema with history.

    Skip this if you state the grain first and pick SCD types without hesitating.

    1. SectionClarify: turning a vague request into a spec7 minQuestions before boxes. Skipping them is a common fail.
    2. SectionDeclaring grain before you build6 minAn unstated grain is the most-cited modeling failure.
    3. InterviewWhat is the grain of a table6 minOne sentence, then an example. Nothing longer.
    4. SectionThe four-step design process7 minProcess, grain, dimensions, facts: a script for the round.
    5. InterviewFact or dimension6 minSounds basic. The follow-up is where it gets hard.
    6. ExerciseSubscription warehouse grain35 minin the studioPick the grain, build the star, defend both.
    7. SectionStar vs snowflake: the trade-offs6 minAsked by name. Know when a snowflake earns its joins.
    8. SectionNatural vs surrogate keys6 minJoining on natural keys is a named failure reason.
    9. ExerciseOrders you can trust eight months later45 minin the studioKeys and totals that survive every join.
    10. SectionChoosing the right SCD type7 minType 1 versus Type 2, argued from what the business needs.
    11. InterviewWalk me through the SCD types6 minName the types, then pick one for a real column.
    12. ExerciseAddresses that stay true to the past40 minin the studioHistory that still answers where they lived at order time.
    13. ExerciseRefunds without double counting45 minin the studioRefunds as facts, without double-counting revenue.
    14. InterviewDesign a financial ledger6 minA full design prompt, taken from a real loop.
    15. SectionNarrating a modeling answer6 minGrain first, then the trade-offs, out loud.
    16. QuestionsData Modeling · Model design15 minDesign prompts. Open every answer with the grain.

  4. Week 04 · ~4h

    Pipeline design: safe to run twice

    You can design a batch pipeline that survives retries, backfills and late data.

    Skip this if you explain idempotent writes and a backfill plan without notes.

    1. SectionThe requirements-first method7 minRequirements before boxes. The happy path alone fails.
    2. SectionThe questions to ask first6 minNot asking is a common reason design rounds fail.
    3. SectionHow to choose without overbuilding6 minStreaming by default reads as over-engineering here.
    4. SectionIdempotency: safe to repeat7 minThe word every design round is waiting to hear.
    5. InterviewAppend, merge or overwrite6 minThe write mode decides what a rerun does.
    6. SectionBackfills: rewriting history safely7 minBug fixed, six months wrong. Plan the rewrite.
    7. SectionWhich runs exist, and when they fire6 minThe logical date trips most candidates. Predict the runs.
    8. ExerciseThe fact arrived first45 minin the studioLate facts, joined without losing or doubling rows.
    9. ExerciseSLA-aware alerting flow40 minin the studioRetries, alerts, and an SLA someone actually owns.
    10. ExerciseFive minutes behind the source45 minin the studioCDC in practice: updates and deletes, minutes behind.
    11. InterviewFail the record or fail the job?6 minQuarantine versus stop: a judgement call they probe.
    12. InterviewDeduplicating with a version6 minDedupe on retry, asked the way a loop asks it.
    13. InterviewChoosing a partition key6 minPartitioning decides what a rerun can safely replace.
    14. ChapterSystem & Pipeline Design20 minHow design rounds are run, and what gets scored.
    15. QuestionsData Pipeline · Failure & recovery15 minSay what a rerun does before they ask.

  5. Week 05 · ~3h

    Spark: the mid-level round

    You can explain shuffles, joins and skew from a Spark plan.

    Skip this if you can say why a join shuffled, and fix it.

    1. InterviewThe three components, named6 minDriver, executors, cluster manager: the warm-up question.
    2. Sectionrepartition vs coalesce6 minAsked by name, and often answered backwards.
    3. SectionAnatomy of a shuffle7 minEvery Spark performance answer starts from the shuffle.
    4. SectionReducing and avoiding shuffles6 minThe fix they want after you name the problem.
    5. SectionBroadcast (map-side) joins6 minThe first fix for a slow join with a small side.
    6. Exercisebroadcast() with auto-broadcast off, and the case where Spark ignores it20 minin the studioAuto-broadcast off, lookup join shuffling. Make broadcast() work anyway.
    7. InterviewLogical vs physical plan6 minReading a plan at a high level is expected.
    8. SectionDetecting skew in the Spark UI6 minSpot the straggler. Designing salting is Senior depth.
    9. ExerciseWhere does skew show up? Reading task metrics, not the DAG20 minin the studioFind skew in task metrics, and where it is absent.
    10. InterviewWalk me through fixing skew6 minMid-level answer: symptoms, AQE, then broadcast.
    11. SectionWhat AQE fixes at runtime6 minKnow what AQE fixes before tuning by hand.
    12. ExerciseIf AQE coalesces anyway, does spark.sql.shuffle.partitions still matter?20 minin the studioWhat the partition setting still controls once AQE is on.
    13. SectionFile formats: columnar vs row6 minParquet versus CSV, argued in one minute.
    14. InterviewWhy small files hurt, step by step6 minA production problem every Spark team has hit.
    15. InterviewHow many partitions for 1 TB?6 minBack-of-envelope sizing, asked with real numbers.
    16. QuestionsApache Spark · Debug & fix15 minDiagnose aloud: symptom, cause, then the fix.
    17. QuestionsApache Spark · Conceptual & architecture15 minFundamentals said cleanly. Deeper internals are Senior depth.

  6. Week 06 · ~4h

    Loop week: the stories and the rehearsal

    You can tell three STAR stories and walk a full loop.

    Skip this if your ownership, incident and conflict stories are rehearsed aloud.

    1. ChapterThe Recruiter & Hiring-Manager Screen20 minYour two-minute story, and the questions to ask back.
    2. ChapterBehavioral Storytelling20 minOwnership, incident and disagreement stories, in STAR form.
    3. InterviewA pipeline incident you owned6 minThe incident story every loop asks for, told end to end.
    4. InterviewA time you made a mistake6 minThe most-asked behavioral prompt. Own the mistake in sentence one.
    5. InterviewA disagreement, and how it resolved6 minDisagree with evidence, then commit. Asked at Amazon, Meta, Google.
    6. InterviewConflicting priorities and deadlines6 minManagerial rounds at product and services firms both ask it.
    7. ChapterLive Coding: SQL & Python20 minHow live rounds are scored beyond a correct answer.
    8. ChapterTake-Home Projects20 minScale-ups and services firms often send one instead.
    9. ChapterAmazon20 minLeadership Principles are scored in every round.
    10. ChapterFlipkart20 minModeling, SQL, then a Spark machine-coding round.
    11. ExerciseFlipkart: a daily snapshot from sparse changes35 minin the studioStretch: a Flipkart-style snapshot question.
    12. ExerciseAmazon: net revenue across two fan-outs30 minin the studioStretch: Amazon-style fan-out, twice over.
    13. QuestionsBehavioral · Ownership & delivery15 minTwenty reported prompts. Point one of your stories at each.

03

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