Data Engineer System Design Problems
Data Engineer System Design Problems
Rubric-scored system design practice problems for data engineer interview prep.
System design practice problems for data engineer interview prep. Each problem mirrors the 45-60 minute interview round format: scenario, clarifying questions, high-level architecture, failure-mode drill per component, cost reasoning, and adapt-on-fly when the rubric flips a requirement.
Practice system design problems for data engineer roles differ from practice SQL or Python problems because there is no auto-grader for "is this architecture correct". Multiple valid designs exist for any scenario. The rubric scores the decision process and the failure-mode articulation, not a specific canonical answer.
Five scenario families anchor the practice catalog. Streaming ingestion: design a pipeline that ingests 10B clickstream events per day with a 15-minute dashboard freshness SLA, where the dashboard sits on top of a BI tool that cannot handle table swaps. Daily warehouse load: design a daily ETL from a 50TB Postgres production database to Snowflake with a 4-hour load window and full backfill capability. ML feature pipeline: design a feature store with online (10ms read latency) and offline (training-time joins) paths for a recommendation model, with feature parity between paths. Reconciliation pipeline: design a daily reconciliation between payment processor settlement reports and internal transaction events, with discrepancy alerting and idempotent re-runs. Multi-region warehouse: design a multi-region active-active warehouse for a global product with regional sovereignty requirements (EU data stays in EU).
Each problem ships with a rubric-scored verdict covering five dimensions. SLA match: does the proposed design meet the freshness, throughput, and latency requirements stated in the scenario. Cost reasoning: back-of-envelope numbers for Kafka shards, Spark workers, Snowflake credits, S3 storage class trade-offs. Failure modes: 3 per component, with detection mechanism and recovery strategy. Tool fit: why this technology (Kafka vs Kinesis vs Pub/Sub) and not the alternative, defended in one sentence. Adapt-on-fly: when the rubric flips a requirement (SLA tightens, volume jumps 100x, downstream constraint added), does the design modify in place or restart from scratch.
Common failure modes the rubric explicitly fails. Skipping clarifying questions (jumping straight to drawing without nailing down throughput, latency, durability requirements). Naming a tool without defending the choice ("I'd use Spark" without saying why over Flink or Dataflow). Missing failure modes (not naming what happens when Kafka brokers die, when Spark executors OOM, when Snowflake MERGE deadlocks). No cost reasoning at L5+ (a senior data engineer should produce rough numbers, not just architecture). Throwing out the design on a mid-round pivot instead of modifying in place.
The senior data engineer who has practiced 5 of these scenarios with rubric review usually arrives at the interview with the failure-mode taxonomy internalized. The most common positive signal is the candidate who names a failure mode the interviewer was about to ask about, before being asked. That moves the rubric from "could answer when prompted" to "anticipates the problem".
- How are data engineer system design problems graded without a single correct answer?
- The rubric scores the decision process and failure-mode articulation, not a specific canonical answer. SLA match (25 percent), cost reasoning (20 percent), failure modes per component (20 percent), tool fit defense (15 percent), adapt-on-fly (20 percent). Multiple valid architectures score well if the data engineer can defend each component choice and name the failure modes.
- What is the most common failure mode in system design practice?
- Skipping clarifying questions. The candidate jumps straight to drawing without nailing down throughput, latency, durability, replay window, and exactly-once requirements. The rubric explicitly weights the clarifying phase; spending 5 minutes there scores above hitting the keyboard immediately.
- How many system design practice problems should I solve before a senior data engineer onsite?
- Five well-practiced scenarios across five domains beats fifteen rushed ones on similar domains. Aim for: clickstream ingestion, daily ETL CDC, ML feature store, payment reconciliation, multi-region warehouse. Each takes 60-75 minutes including rubric review. Finish all five over 3 weeks.
- What is the cost reasoning expectation at L5+?
- Rough back-of-envelope numbers, not exact pricing. For 10B events per day: throughput is 116k events per second average, peak 5x; with 1KB per event that is 116 MB/s average, 580 MB/s peak; on Kinesis at 1MB/s per shard that is 116-580 shards depending on key distribution. For Snowflake: cost per TB scanned versus slot reservation. For S3: storage class trade-offs. Aim for order-of-magnitude correctness.
- How should I handle the mid-round requirement flip?
- Modify the existing design in place. Articulate what changes and what stays. SLA tightens from 15 min to 1 min: move from Spark Structured Streaming micro-batch to Flink streaming. Volume jumps 100x: increase Kafka shards, repartition Spark jobs, review broadcast vs sort-merge decisions. Throwing out the design and restarting is the L4 signal; modifying in place is the L5 signal.
- Do I need to know specific cloud providers?
- Depends on the company. Amazon expects AWS-native (Kinesis, Glue, EMR, S3, Athena, Redshift). Google expects GCP-native (Pub/Sub, Dataflow, BigQuery, Dataproc). Most other companies stay vendor-neutral. Practice both AWS-native and GCP-native variants of clickstream and CDC designs; you will use one or the other depending on the company.
- How long is a typical data engineer system design round?
- 45 to 60 minutes for one round. Senior+ loops sometimes include a second design round (platform-level meta-question). The 45-minute version: 5 minutes clarifying, 15 minutes high-level architecture, 20 minutes failure-mode drill, 5 minutes adapt-on-fly. Pacing matters: spending 30 minutes on the high-level means no time for the drill.
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.