Data Pipeline Practice Problems
Data Pipeline Practice Problems
End-to-end data pipeline practice problems for data engineer interview prep.
Data pipeline practice problems for data engineer interview prep. End-to-end design across ingest, transform, and serve layers. Scenarios include 10B-event-per-day clickstream, daily warehouse load, ML feature pipeline, payment reconciliation, multi-region active-active warehouse. Rubric-scored verdicts mirror the senior data engineer pipeline design rubric.
Data pipeline practice problems for data engineer roles cover six scenarios that recur in 2026 interview reports. Each problem is a 60-75 minute exercise: 45-60 minutes for the design, 15 minutes for rubric review. The rubric scores ingest design, transform design, serve design, idempotency at each layer, failure-mode articulation, and cost reasoning.
Scenario one: 10B-event-per-day clickstream. Web SDK to local buffer to CDN-fronted ingest to Kafka (24 partitions) to Spark Structured Streaming (1-min trigger) to Parquet on S3 (date/hour partition) to dbt micro-batch hourly to gold star schemas in Snowflake. Failure modes: SDK buffer overflow, CDN edge failure, Kafka broker death, consumer lag, late-arriving events. Cost: Kafka shards, Spark workers, Snowflake credits per query.
Scenario two: daily Postgres-to-Snowflake CDC. Debezium to Kafka to S3 raw immutable to Spark daily ETL to Snowflake MERGE INTO. Idempotency via run_id and composite-key MERGE. Failure modes: Debezium falls behind, schema change at source, dedup misses late event, MERGE deadlock. Backfill via insert-overwrite per partition or MERGE with fresh run_id.
Scenario three: ML feature store. Real-time path uses Flink to Redis with 10ms reads. Batch path uses Spark to S3 Parquet to Feast catalog. Training uses as-of joins with feature_ts less-than-or-equal-to label_ts to prevent leakage. Failure modes: Flink and Spark compute features differently, Redis OOM, training-serving skew. Solution: centralized feature library shared between both paths, eviction policy on Redis, feature distribution monitoring with drift alerts.
Scenario four: daily payment reconciliation. Postgres transactions via Debezium to Kafka to S3 bronze. Settlement reports delivered via SFTP nightly land in S3. Spark daily reconciliation reads both, joins on (txn_id, settlement_id), produces reconciled fact with status and discrepancy. Snowflake MERGE on (txn_id, run_id). Failure modes: settlement file late or missing, discrepancy alert false positive, MERGE deadlock with concurrent writer.
Scenario five: multi-region active-active warehouse. Region-local writes with async cross-region CDC replication. Conflict resolution via last-writer-wins for ordered data or CRDT for counters. SLA tiers: real-time within region, eventually-consistent across regions. 2x storage minimum cost. Failure modes: region failure (regional failover, RPO and RTO), cross-region lag, conflict resolution edge cases. Most companies do not need multi-region; senior data engineer L6+ rubrics test the design anyway.
Scenario six: real-time analytics dashboard. Spark Structured Streaming with 1-minute trigger reads Kafka, aggregates, writes to Druid or ClickHouse for sub-second BI query latency. Hourly Spark batch from Kafka to S3 to Snowflake for historical. Materialized views in Snowflake refresh on schedule for dashboard queries. Failure modes: Druid hot partition, Spark micro-batch lag, dashboard query timeout on cold cache.
Each problem ships with a rubric verdict that identifies what scored well and what was missed. The gap analysis between candidate solution and rubric verdict is where practice value compounds.
- How are data pipeline practice problems graded?
- Rubric-scored on six dimensions: ingest layer design (mechanism, sizing, durability), transform layer design (engine choice, idempotency, late-arriving handling), serve layer design (warehouse, feature store, materialized view), failure-mode articulation per component, cost reasoning, and adapt-on-fly. Multiple valid architectures score well if the data engineer can defend each component choice.
- How long does a data pipeline practice problem take?
- 60-75 minutes including rubric review. 45-60 minutes for the design exercise, 15 minutes for the rubric verdict comparison and gap analysis.
- What scenarios are most common in data pipeline practice?
- Six recur most: 10B-event-per-day clickstream, daily Postgres-to-Snowflake CDC, ML feature store with online and offline, daily payment reconciliation, multi-region active-active warehouse, real-time analytics dashboard. Each appears across multiple companies' data engineer interview reports.
- Do these practice problems test specific cloud vendors?
- Most stay vendor-neutral or offer multiple variants. AWS-native variants for Amazon prep (Kinesis, Glue, EMR, S3, Athena, Redshift). GCP-native variants for Google prep (Pub/Sub, Dataflow, BigQuery, Dataproc). Spark+Iceberg variants for Netflix and Databricks prep. Practice both AWS-native and GCP-native variants of clickstream and CDC.
- What is the most common failure mode in pipeline practice?
- Spending too long on the high-level architecture and running out of time for failure-mode drills. The rubric weights failure modes per component at 20 percent; skipping them is a guaranteed sub-L5 score. Pace the round: high-level in 15-20 minutes, failure modes for 20-25 minutes.
- How many pipeline practice problems should I solve before a senior data engineer onsite?
- Six well-practiced scenarios across the six recurring scenario shapes beats fifteen rushed similar ones. Aim for one of each over 3 weeks. The signal interviewers test is whether the data engineer can transfer the pattern to a new combination of source, transform, and serve.
- Do these practice problems include the cost reasoning at L5+ rubric weight?
- Yes. Each problem's rubric verdict includes back-of-envelope cost numbers: Kafka shards at $X per shard-hour, Spark workers at $Y per worker-hour, Snowflake credits at $Z per credit, S3 storage class trade-offs. Compare your numbers to the rubric's; order-of-magnitude correctness counts.
- How does the adapt-on-fly part of practice work?
- Each problem's rubric includes a 'pivot' section: a requirement that changes halfway through (SLA tightens, volume jumps, downstream constraint added). Walk through how your design would change. The L5 signal is articulating what changes and what stays in the existing design without throwing it out.
124 practice problems matching this filter. Difficulty: medium (57), hard (67).
Pipeline Architecture (124)
- 45 Minutes Turned Into 3.5 Hours - medium - Spark jobs are running. Just not fast enough.
- 600 Million Events a Day - hard - 600 million events a day. Two years of retention.
- A Clean Number for Every Merchant - hard - Raw payment logs in. Clean merchant summaries out.
- A Million Cars Phoning Home - hard - Every vehicle is a sensor. Deploy the pipeline to catch it all.
- Analysts Are Slowing the Store Down - medium - Orders placed. Data warehouse hungry.
- A New Column on a Billion Rows - hard - Add and backfill a new column to a billion-row production table with zero downtime.
- A Shared Drive Full of Contracts - medium - Buried in PDFs. The data is in there somewhere.
- A Stream All Day and a File at Midnight - hard - Real-time and batch. Same pipeline. No compromises.
- Badging Items That Already Sold Out - hard - Same-day delivery. The features have to be faster.
- Basel, CCAR, and Monday Morning - medium - The regulator does not accept 'eventually consistent.'
- Bikes Before Rush Hour - hard - Bikes in, bikes out. The city needs to predict demand.
- Credit for Every Touch - medium - They saw the ad, clicked the email, then bought. Who gets credit?
- Doubling Every Six Months - hard - Tuesdays are quiet. Black Friday is not.
- Eight-Hour-Old Positions - hard - Positions shift by the second. The math cannot lag.
- Eight Teams, Eight Latencies - medium - Millions of gamers. The architecture decision changes everything.
- End of Day Is Too Late - medium - Every swipe tells a story.
- Equities, ETFs, and the SEC - hard - Fractional shares, multi-currency, point-in-time. All of it.
- Event System for Multiple Consumers - hard - One event, many hungry consumers.
- Every Dataset Needs a Paper Trail - hard - The FDA has opinions about your data pipeline.
- Every Deal Is a Financial Transaction - hard - Real money on the table. Reconstruct every hand.
- Every Device, Every Impression - hard - Every ad seen. Every second watched. Real-time.
- Every Device Has Its Own Dialect - medium - Three sources. Three formats. Same workout.
- Every Firm Formats It Differently - medium - The regulator changed the format. Again. Handle it.
- Every Format Imaginable - hard - PDFs, HL7, JSON. All of it lands in the same lake.
- Everyone Wants the Same Data, Differently - hard - How you store it decides how fast you can read it.
- Every Region Exports Its Own Way - medium - Sales data, BigQuery, Dataflow. Make it all sing.
- Every Scan, Every Parcel, Every Pin Code - medium - Out for delivery. Delivered. Except the events arrived backwards.
- Fifty Thousand Retailers - medium - Retail data at CPG scale. Every SKU, every store.
- The Box That Won't Fit the Data - hard
- Five Times the Traffic, Five Times the Bill - hard - Scale up when needed. Do not bankrupt the team.
- Five Years of Cron Jobs - hard - Half the jobs run on cron. Half run on events. All of it has to move.
- Flying Blind Until Midnight - hard - Intraday risk, full lineage. The regulator is watching.
- Four Teams, One Topic, No Agreement - hard - Everybody is writing to it. Nobody documented it. Now production is fragile.
- Greenfield Build for Six Sources - hard - Infrastructure as code. Meaning as a service.
- Half a Million Rental Cars - medium - Every vehicle is reporting. Every rental matters.
- The Identity Problem - hard - Old systems. New demands. The same customer appears under three different names.
- Listens From Everywhere, Counted Once - hard - Phones, tablets, laptops. And some of them report late.
- Live Viewers, Live Billing - hard - The stream is live. The data cannot wait.
- Near-Real-Time Trending Dishes Dashboard - hard - The dish rankings update faster than the kitchen.
- Nested Docs, Flat Reports - medium - Two databases. One direction. No data left behind.
- Nightly Exports Are Too Slow - medium - Healthcare claims change constantly. The warehouse cannot fall behind.
- 4,500 Stores Before Sunrise - medium - The shelves open at 7. The data better be there.
- Not Every Team Can See Every Row - hard - Everyone can see the bucket. Not everyone should.
- One Bill Across Three Clouds - medium - AWS, Azure, GCP. Three bills. One truth.
- One Earthquake, Ten Thousand Tweets - hard - The firehose is on. Separate signal from noise.
- Out of the Data Center - medium - The on-prem servers are not getting any younger.
- The Speed Layer - medium - Dashboards can't wait for raw logs. Something has to happen upstream.
- Prove the Number Is Right - hard - Bad data in fintech is not just messy. It is expensive.
- Real Data, Fake Patients - hard - Dev needs production data. HIPAA says absolutely not.
- The Register Never Sleeps - medium - Every swipe lands in the warehouse. The table has to stay current without breaking.
- Recommendations Now, Royalties Later - medium - The catalog updated. Did anyone notice?
- Replicate It Without Breaking It - hard - The source changed. The lake needs to know immediately.
- Risk Models on Week-Old Data - medium - Loan approved. Loan denied. Every decision is an event.
- SaaS API Connector with Incremental Sync - medium - The API has rate limits. You have deadlines.
- Same-Day Sales, Every Store - medium - The cash register data needs to be queryable by morning.
- The Living Table - medium - Data lands continuously. History must survive every update.
- Score It Before It Clears - hard - The fraudsters move fast. Your pipeline has to move faster.
- Ship Before Fraud Finishes Checking - hard - The claim looks clean. The fraud model disagrees.
- Six Hours to Miss a Deadline - medium - The rebuild works. It just doesn't finish in time.
- Six Hours to Refresh Every Number - medium - Ratings change. The incremental model has to keep pace.
- Six Million Rows Before the Market Opens - medium - One massive CSV. Millions of timestamps.
- Six Sources, One Platform - medium - ADF orchestrates. Unity Catalog governs. Nothing leaks.
- Sixty Minutes, Every Hour - medium - Every hour, on the hour. No excuses.
- Stores and the Site, Together - hard - The registers never stop ringing.
- Store, Site, and Distributor - medium - Sales data is piling up. Someone has to make sense of it.
- The Acquisition Still Taking Bookings - hard - Two systems, two schemas. One truth.
- The Agency That Changes the Columns - medium - The schema changed overnight. Again.
- The Analysts Cannot Touch Production - medium - Production is the source. Analytics needs its own copy.
- The Analyst Who Saw the Salary Data - hard - Two incidents. One shared lake. The access model was never designed, just assumed.
- The API Drip Feed - medium - The API gives you 100 records at a time. You need millions.
- The Bad Row That Broke the Dashboard - medium - Bad records cannot reach the warehouse.
- The Binding and the Claim - medium - Policies are instant. Claims take their time.
- The Booking That Came Three Ways - hard - PMS, OTA, and website all think they took the reservation first.
- The Boutique That Sold in Six Currencies - hard - Every sale is real. The rate it was converted at depends on who is asking.
- The Bucket Full of Resumes - medium - A thousand resumes. Structured data inside each one.
- The Carrier Moving to Azure - medium - Claims arrive messy. The medallion cleans them up.
- The Claim That Picks Its Own Lane - medium - Three entry points. Different workflows. All must route correctly.
- The Clicks We Throw Away - hard - Every tap, swipe, and scroll. At scale.
- The Clock That Runs Two Ways - hard - Nightly batch and live events. One dashboard.
- The Consent Stitcher - medium - Consent was given. Or was it? Stitch the records together.
- The Dashboard and the Attribution Model - hard - Streaming and batch. One pipeline to rule them.
- The Decision Before the Door Closes - hard - The window to stop it is smaller than you think.
- The Distributor Filing Problem - medium - Hundreds of suppliers. One warehouse. One deadline.
- The Event Pile - hard - 600 million clicks a day. The budget is not infinite.
- The Fare Aggregator - medium - Airfares shift every minute. Catch the best ones.
- The Fleet That Never Stops - hard - Every truck is talking. Not everyone can hear them yet.
- The Leaderboard That Costs $25K a Month - hard - Product wants it live. Engineering has a price tag.
- The Meal Kit That Knows You - medium - What they ordered says a lot about what they want next.
- The Migration That Cannot Break Morning - hard - It all works today. Moving it without losing a single report is the hard part.
- The Models Going Stale - hard - The model is only as good as what you feed it.
- The Panel and the Set-Top Boxes - hard - Set-top boxes tell you who watched. Projection tells you how many.
- The Patients We Cannot Move - hard - Patient data stays local. Insights have to be global.
- The Points Arrive Two Days Late - medium - The bank data shows up late. The rewards were already sent.
- The Provider That Sometimes Sleeps - medium - The models run at dawn. The data has to be there first.
- The Query That Used to Be Fast - medium - Queries used to be fast. Something changed.
- The Queue That Wouldn't Stop Growing - medium - 500,000 messages behind and the number keeps climbing.
- The Revenue That Was Wrong for Two Weeks - medium - Nobody caught it until the CFO asked a question. Design the system that catches it first.
- The Sale That Needs to Land Now - medium - Three channels feeding one view. Not all of them speak the same language.
- The Signals That Power Recommendations - medium - Fresh signals, many teams, one pipeline.
- The User Who Asked to Be Forgotten - hard - Users want their data erased. Completely.
- The Vendor Who Never Warns You - medium - Every month, something is different. The dashboards have no idea.
- The What-If Machine - hard - A million slots. A thousand campaigns. Every combination matters.
- The Whiteboard Exercise - medium - Marker in hand. Draw the whole thing.
- Thirty Cities, One Forecast - hard - Five cities. Five data formats. One prediction.
- Thirty Countries, One Solvency Number - hard - Premiums collected globally. Losses happen locally.
- Thirty Million Unique Jobs a Year - hard - One press run, many orders. Group them right.
- Thousands of Practices, One Dataset - hard - Patient records in, operational insights out.
- Three Providers, One Workout - hard - The same ride, reported three times.
- Three Regions, One Finance Team - hard - Payments from everywhere. One consistent report.
- Three Regions, One Report - hard - Three regions, billions of payments, one merchant summary by 6 AM.
- Towers and Phones, Same Story - hard - Tower signals meet app events. Somewhere in between is the truth.
- Traders, Risk, and the Regulators - medium - Markets move in milliseconds. The pipeline has to keep up.
- Two Hundred Million Redirects - medium - Billions of clicks. One tiny code. Two very different clocks.
- Two Million Boxes by Monday Morning - hard - Shipped, maybe. Delivered, debatable.
- Two Systems, One Room Count - hard - Two booking systems. Rooms do not duplicate themselves.
- Two Ways to Catch a Change - medium - Two ways to watch the database. Each has a cost.
- Two Years of Every Click - hard - Every click, every aisle, every day for two years.
- Two Years of Clicks, Cheap - hard - Two years of clicks. Every query has to be affordable.
- What Everyone Is Watching - hard - Someone is watching. Capture everything.
- What Should We Recommend Tonight - hard - They ordered pad thai twice. That means something.
- Where Is Every Truck, Right Now - medium - Trucks are moving. Every ping counts.
- Which Promotion Is Actually Working - hard - Was the promotion worth it? The data knows.
- Who Is Churning and Why - medium - Subscribers churn. The pipeline cannot.
- Who Saw the Ad Twice - hard - TV and digital. Same viewer, two measurement worlds.