ETL Design Interview Prep
ETL Design Interview Prep
Prep for the ETL design portion of a data engineer interview loop.
ETL design interview prep for data engineer roles. Idempotent ETL with run_id baked into output partitions. MERGE INTO on composite natural key for upsert. Schema evolution with additive-only contracts. Late-arriving data with MERGE-ADD-not-REPLACE. Orchestration with Airflow or Dagster. The patterns that compose 80 percent of data engineer ETL design rounds in 2026.
ETL design questions in 2026 data engineer interviews center on five concerns. Idempotency: a re-run of yesterday's ETL must produce the same answer as the original run. The standard pattern is run_id baked into output partitions plus MERGE INTO on composite natural keys; restarting a failed Spark job overwrites the same partition with the same result. Schema evolution: source schemas change; ETL pipelines must propagate additive changes (new nullable columns) automatically and refuse to deploy breaking changes (renamed columns, type narrowing). Schema registries (Confluent Schema Registry for Avro, AWS Glue Schema Registry for Parquet) enforce contracts at the producer side; downstream bronze layer keeps the raw payload for replay.
Late-arriving data handling. Events arrive at the ETL pipeline after their event_time has passed: refunds processed days after the original transaction, click events from offline mobile devices, conversions in the 28-day attribution window. The standard pattern is MERGE INTO with ADD semantics, not REPLACE. MERGE INTO daily_revenue d USING (SELECT date, SUM(amount) AS revenue FROM events WHERE processed_at greater-than-or-equal-to since GROUP BY date) src ON d.date = src.date WHEN MATCHED THEN UPDATE SET revenue = d.revenue plus src.revenue WHEN NOT MATCHED THEN INSERT. The ADD (revenue = existing plus new) preserves yesterday's correct total when only late events arrive today; REPLACE would zero out the old total.
Orchestration choice. Airflow is the incumbent (Python-native DAGs, large operator ecosystem, mature but heavy). Dagster is the modern alternative (asset-based, software-defined assets concept, better type safety). Prefect for lighter workflows. dbt is not an orchestrator but is the de-facto in-warehouse transformation tool, called by Airflow or Dagster. Data engineer interview rounds rarely demand a specific orchestrator; what matters is the dependency model (asset-based, task-based) and the retry-on-failure semantics.
Backfill capability is the senior data engineer ETL question. A backfill re-runs ETL for a past date range to correct an error, fill a gap, or add a new column to historical data. The design constraint: backfills must not corrupt downstream consumers reading from the same tables. Two patterns. Insert-overwrite per partition: the backfill writes to a staging partition, then atomically swaps with the production partition. MERGE INTO with run_id: the backfill writes with a fresh run_id; downstream queries filter on the latest run_id per partition. Both require explicit partition design and tend to fail on non-partitioned tables.
Schema-on-read versus schema-on-write. Schema-on-write enforces the schema at ingest (parquet files with strict types, warehouse tables with column constraints). Schema-on-read defers enforcement to query time (raw JSON in S3, parsed at SELECT). Modern data engineer pipelines use schema-on-write for silver and gold layers (typed, validated, business-rule-applied) and schema-on-read for bronze (raw source format preserved for replay).
Companies whose data engineer interviews emphasize ETL design: Stripe (idempotent reconciliation, financial-data audit), Airbnb (ETL framework Airflow was created here; deep ETL expertise expected), Uber (large-scale Spark and Hive ETL), Snowflake and Databricks (vendor-specific ETL patterns).
- What is the idempotent ETL pattern?
- Run_id baked into output partitions plus MERGE INTO on composite natural key. A failed ETL job that restarts overwrites the same partition with the same run_id, producing the same result. The principle is that re-running yesterday's ETL today must produce yesterday's answer. Without idempotency, retries corrupt data and on-call data engineer responses become risky.
- How does a data engineer handle schema evolution in an ETL pipeline?
- Schema registry (Confluent Schema Registry for Avro, AWS Glue Schema Registry for Parquet) at the producer side. Additive changes (new nullable column) propagate automatically. Breaking changes (renamed column, narrowed type) refuse to deploy. Downstream bronze layer keeps the raw payload as JSON or original format for replay after writing a new mapping. Producer-side contract tests in CI catch breaking changes before deploy.
- How does ETL handle late-arriving data?
- MERGE INTO with ADD semantics, not REPLACE. MERGE INTO daily_revenue d USING src ON d.date = src.date WHEN MATCHED THEN UPDATE SET revenue = d.revenue + src.revenue. The ADD preserves yesterday's correct total when late events arrive; REPLACE would zero out the old total. Conversion windows (28 days for ads attribution) require this pattern by default.
- What is the difference between Airflow and Dagster?
- Airflow is the incumbent: Python DAGs, task-based, large operator ecosystem, mature but operationally heavy. Dagster is asset-based with software-defined assets concept and better type safety. Prefect targets lighter workflows. Data engineer interview rounds rarely demand a specific orchestrator; what matters is the dependency model and retry semantics. Mention both as options.
- How does a data engineer design backfill capability?
- Two patterns. Insert-overwrite per partition: backfill writes to a staging partition, then atomically swaps with the production partition. MERGE INTO with run_id: backfill writes with fresh run_id; downstream queries filter on latest run_id per partition. Both require explicit partition design. Non-partitioned tables generally cannot be safely backfilled without coordination.
- What is schema-on-read versus schema-on-write?
- Schema-on-write enforces schema at ingest: parquet files with strict types, warehouse tables with column constraints. Schema-on-read defers enforcement to query time: raw JSON in S3 parsed at SELECT. Modern data engineer pipelines use schema-on-write for silver and gold layers (typed, validated) and schema-on-read for bronze (raw source format preserved for replay).
- How long does the senior data engineer ETL design round take?
- 45 to 60 minutes typically. The scenario usually combines: a specific source database or stream, a freshness SLA, an idempotency requirement, a schema evolution constraint, and a downstream consumer with quirks (BI tool that cannot handle table swaps, ML serving layer that needs feature parity). Senior rubrics weight idempotency design, schema evolution story, and backfill capability.
- When does a data engineer choose ETL versus ELT?
- ETL transforms before landing in the warehouse: useful when the warehouse is expensive to scan or when source data is genuinely too large to land raw. ELT lands raw, transforms in-warehouse: dominates in 2026 because columnar warehouse compute is cheap and the raw bronze layer enables replay. Most modern data engineer pipelines are ELT with dbt or Spark on the bronze layer.
144 practice problems matching this filter. Difficulty: medium (71), hard (72), easy (1).
Pipeline Architecture (144)
- 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.
- A Million Moving Dots - medium
- 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.'
- Before the Batch Is Lost - hard
- 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?
- Disappearing Ink - easy
- Everything Lands, Then It Ships - medium
- 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.
- Every Version of You - medium
- Fifty Thousand Retailers - medium - Retail data at CPG scale. Every SKU, every store.
- 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.
- Fresh and Forever - medium
- 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.
- Mark to Market - medium
- 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.
- Seconds and Months - medium
- Seconds to Trend - medium
- 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.
- Someone Else's Server - hard
- 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 Early Warning - medium
- 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 Firehose and the Ledger - hard
- 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 Ledger and the Live Wire - hard
- The Meal Kit That Knows You - medium - What they ordered says a lot about what they want next.
- The Metric That Moved - medium
- 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 Morning File - medium
- The Next Track - medium
- 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 Same Stream Twice - hard
- The Signals That Power Recommendations - medium - Fresh signals, many teams, one pipeline.
- The Thirty-Second Rule - medium
- Counted Once, Remembered Forever - hard
- 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 Sources of Truth - medium
- 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.
- Where the Crowd Goes - medium
- 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.