ELT Interview Questions
ELT Interview Questions
ELT pattern questions for data engineer interview prep.
ELT interview questions for data engineer roles. Extract, load raw to warehouse, transform in-warehouse with dbt or Spark. Bronze, silver, gold layers. Idempotent MERGE INTO on composite natural keys. The dominant pattern in 2026 data engineer pipelines.
ELT (extract, load, transform) is the dominant data engineer pipeline pattern in 2026. The change from ETL to ELT happened over the late 2010s and early 2020s as columnar warehouse compute became cheap enough that transforming inside the warehouse beat transforming in flight. The modern stack: extract from source (CDC via Debezium, batch SELECT, API pull), land raw immutable in the warehouse bronze layer (Snowflake variant or external table to S3 Parquet), transform in-warehouse with dbt (SQL-based) or Spark (Python or Scala).
Six ELT-specific concerns appear in data engineer interview rounds. Bronze layer design: raw immutable, schema-on-read for flexibility, partitioned by load_date for replay. Silver layer design: cleaned, typed, deduplicated, conformed dimensions. Gold layer design: business-ready star schemas with aggregates and materialized views. dbt versus Spark for transformation: dbt is SQL-only, runs in the warehouse, has the better developer experience for analytical transformations; Spark is general-purpose, handles non-SQL workloads (ML feature engineering, complex Python logic), more operational overhead. Most modern data engineer pipelines use both: dbt for analytics, Spark for ML and heavy transforms.
Idempotency in ELT. Bronze layer is append-only with load_date partition; re-running an extract for a date overwrites the same partition. Silver and gold use MERGE INTO on composite natural keys with run_id. dbt's incremental materialization handles this pattern automatically with the unique_key config option. Spark with Delta or Iceberg MERGE INTO similarly. The senior data engineer signal is articulating idempotency at every layer, not just at the top.
Schema evolution in ELT. The bronze layer's raw immutable nature handles additive changes naturally: new columns flow into bronze without code change. Silver layer transformations need explicit schema handling: dbt source freshness checks plus schema tests catch breaking changes; Spark schema-on-read with explicit column references propagates additive changes and breaks on rename. Schema registries at the producer side enforce contracts.
The dbt-specific patterns appear in data engineer interview rounds where the company uses dbt heavily (most 2026 startups, many enterprise teams). Incremental models with the unique_key config for idempotency. Snapshots for SCD Type 2 dimension tracking. Sources with freshness tests. Macros for cross-project DRY (a custom_dedup macro that wraps ROW_NUMBER for the team's standard dedup logic). Tests (not_null, unique, accepted_values, custom singular tests) gating deploys.
Companies whose data engineer interviews emphasize ELT and dbt: Stripe (dbt-heavy), Airbnb (Airflow + dbt-equivalent internal tool), most modern startups (Snowflake or BigQuery + dbt Cloud), Snowflake itself (vendor-specific dbt patterns), Databricks (Delta + dbt or Spark).
- What is the difference between ETL and ELT?
- ETL transforms in flight before landing clean data in the warehouse. ELT lands raw data in the warehouse first (bronze layer) and transforms in-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 too expensive or source-system loading is unacceptable.
- Why has ELT become the default for data engineer pipelines?
- Three reasons. Columnar warehouse compute (Snowflake, BigQuery, Redshift, Databricks) is cheap enough to transform in-warehouse without breaking the budget. The raw bronze layer enables replay: a bug in silver does not require re-ingesting from the source. The dbt ecosystem and similar in-warehouse tools have matured to the point that SQL-based transformation is more productive than custom Spark code for most workloads.
- When does a data engineer pick dbt versus Spark for in-warehouse transformation?
- dbt for analytical transformations: aggregations, dimensional modeling, business-rule applications, dashboards. SQL-only, runs in the warehouse, fast developer iteration. Spark for non-SQL workloads: ML feature engineering, complex Python logic, very large transforms that benefit from Spark's distributed compute. Most modern data engineer pipelines use both: dbt for analytics, Spark for ML and heavy transforms.
- What is the bronze, silver, gold layer pattern in ELT?
- Bronze: raw immutable from source, partitioned by load_date, schema-on-read for flexibility. Silver: cleaned, typed, deduplicated, conformed dimensions, schema-on-write. Gold: business-ready star schemas with aggregates and materialized views, optimized for query. Each layer has its own ownership and quality contract; a bug in silver does not require re-ingesting from source.
- How does dbt handle idempotency?
- Incremental models with the unique_key config option. The model's transformation logic produces rows keyed by unique_key; dbt MERGE INTO the destination table replacing or upserting matching rows. Re-running with the same source data produces the same destination. dbt also supports the insert_overwrite materialization for partition-replace idempotency.
- What is a dbt snapshot and when does a data engineer use it?
- dbt snapshot is the built-in SCD Type 2 mechanism. The snapshot captures the state of a source table at the snapshot time and tracks changes with valid_from, valid_to columns. Re-running the snapshot adds new rows for any source changes. Useful for tracking slowly-changing dimensions (customer attributes, product catalog) without writing custom merge logic.
- How does ELT handle schema evolution?
- Bronze layer's raw immutable nature handles additive changes naturally: new columns flow into bronze without code change. Silver layer transformations need explicit schema handling: dbt source freshness checks and schema tests catch breaking changes; Spark schema-on-read with explicit column references propagates additive and breaks on rename. Schema registries enforce contracts at producer.
- What is the difference between dbt Cloud and dbt Core?
- dbt Core is the open-source CLI tool; runs on any infrastructure. dbt Cloud is the managed service with web UI, scheduling, alerting, IDE, CI integration. Most data engineer interviews are stack-agnostic about which dbt; the patterns are the same. Mention dbt Cloud if the company uses it (most cloud-first companies do).
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.