ETL Interview Questions
ETL Interview Questions
ETL pattern questions for data engineer interview prep.
ETL interview questions for data engineer roles. Extract transform load patterns from operational databases to analytical warehouses. Idempotent design with run_id and MERGE. Schema evolution with additive contracts. Late-arriving data with MERGE-ADD semantics. Orchestration with Airflow and Dagster.
ETL (extract, transform, load) is the foundational pattern of data engineer work. Source databases (Postgres, MySQL, MongoDB, Salesforce, third-party APIs) are extracted; the data is transformed (joined, deduplicated, aggregated, conformed to a target schema); the result is loaded into a warehouse (Snowflake, BigQuery, Redshift, Databricks). In 2026, ETL has largely been displaced by ELT (extract, load, transform in warehouse) for most workloads, but the term remains in interview rounds and the underlying patterns persist.
Six ETL patterns appear most in data engineer interviews. Full-refresh ETL: read the entire source, truncate the destination, insert all rows. Simple but expensive at scale; useful for small dimensions and reference data. Incremental ETL with high-water-mark: SELECT WHERE updated_at greater than last_run_max. Captures changes since last run; misses soft deletes and intermediate updates. CDC-based ETL: Debezium watching source WAL, emit changes to Kafka, downstream consumes (the modern default). Snapshot-and-diff: full snapshot at intervals, diff against previous snapshot for changes. Useful when CDC is unavailable but full-refresh is too expensive. SCD merge ETL: incremental ETL with SCD Type 2 update logic on dimension tables. Backfill ETL: re-run a date range to fill gaps or correct errors; requires idempotent design.
Idempotency in ETL. The standard pattern is run_id baked into output partitions or MERGE INTO on composite natural keys. A run_id is a stable identifier per ETL execution (typically run_date plus a sequence). Output partitions are keyed by run_id so a re-run overwrites the same partition with the same result. MERGE INTO on composite keys (pk, run_id) upserts to a target table where re-running produces the same upsert result. Without idempotency, retrying a failed ETL job corrupts data and the data engineer on-call response becomes risky.
Schema evolution in ETL. Source schemas change: a new column is added, a column is renamed, a type is widened. ETL pipelines must propagate additive changes (new nullable column flows to bronze, silver, gold without code change) and refuse to deploy breaking changes (renamed column, narrowed type would break downstream consumers). Schema registries (Confluent Schema Registry for Avro, AWS Glue Schema Registry for Parquet) enforce contracts at the producer side. Downstream bronze keeps the raw payload as JSON or original format for replay after a new mapping is written.
Orchestration in ETL. Airflow is the incumbent (Python DAGs, large operator ecosystem). Dagster is the asset-based alternative (better type safety, software-defined assets). Prefect for lighter workflows. dbt is not an orchestrator but is the de-facto in-warehouse transformation tool called by Airflow or Dagster. The orchestrator's job is to schedule, retry on failure, and propagate dependencies; the orchestrator should never contain transformation logic itself.
Companies whose data engineer interviews emphasize ETL: Airbnb (Airflow was created here; ETL expertise expected), Uber (large-scale Spark and Hive ETL), Snowflake and Databricks (vendor-specific patterns), Stripe (idempotent reconciliation), traditional enterprise (Inmon-style ETL into integrated data warehouse).
- What is the difference between ETL and ELT?
- ETL transforms data in flight before landing in the warehouse (clean data only). ELT lands raw data in the warehouse first (bronze layer) and transforms inside the warehouse with dbt or Spark. ELT dominates in 2026 because columnar warehouse compute is cheap and the raw bronze layer enables replay. ETL persists for use cases where landing raw is genuinely too expensive (massive event streams) or when source-system loading is unacceptable.
- What is the idempotent ETL pattern?
- Run_id baked into output partitions plus MERGE INTO on composite natural keys. A failed ETL job that restarts overwrites the same partition with the same run_id, producing the same result. The principle: re-running yesterday's ETL today must produce yesterday's answer. Without idempotency, retries corrupt data and on-call response becomes risky.
- How does a data engineer handle schema changes in ETL?
- Schema registry at the producer side. Additive changes propagate automatically. Breaking changes refuse to deploy. Bronze layer keeps the raw payload as JSON for replay after writing a new mapping. Producer-side contract tests in CI catch breaking changes before deploy. The schema-evolution story is a senior data engineer ETL rubric item.
- What is the high-water-mark pattern for incremental ETL?
- SELECT WHERE updated_at greater than last_run_max. The ETL job tracks the maximum updated_at it has processed; the next run picks up from there. Simple and cheap. Misses soft deletes (the row is gone from the source). Misses intermediate updates (only the latest state is visible). CDC handles both cases at the cost of operational complexity.
- When should a data engineer use full-refresh ETL?
- For small dimensions (under 1M rows) and reference data (countries, currencies, lookup tables). Truncating and reloading is simple, side-effect-free, and avoids the complexity of incremental tracking. For fact tables or large dimensions, full-refresh is too expensive; use incremental or CDC.
- What is backfill ETL and how is it designed?
- Backfill re-runs ETL for a past date range to fill gaps or correct errors. Two patterns. Insert-overwrite per partition: backfill writes to staging, atomically swaps with production partition. MERGE INTO with run_id: backfill writes with fresh run_id, downstream queries filter on latest run_id. Both require explicit partition design.
- What orchestrator should a data engineer pick for ETL?
- Airflow for incumbent ecosystem and large operator library. Dagster for asset-based modeling and type safety. Prefect for lighter workflows. dbt as the in-warehouse transformation tool called by any of them. The orchestrator's job is to schedule, retry, and propagate dependencies; the orchestrator should not contain transformation logic.
- How does ETL fit into a medallion architecture?
- ETL feeds the bronze layer (raw immutable). Subsequent transformations (silver: cleaned and conformed; gold: business-ready star schemas) are ELT done in-warehouse by dbt or Spark. The bronze layer enables replay: a bug in silver does not require re-ingesting from the source.
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.