FAANG Data Engineer Interview Questions
FAANG Data Engineer Interview Questions
FAANG-tagged data engineer interview questions with per-company rubrics.
FAANG data engineer interview questions tagged from reported Meta, Amazon, Google, Netflix, and Apple loops. Each company's rubric is different. Meta weights communication. Amazon weights Leadership Principles. Google weights complexity reasoning. Netflix weights Spark. Apple weights warehouse architecture. The catalog is filterable down to a single company once you know your target.
FAANG (Meta, Amazon, Apple, Netflix, Google) data engineer interview loops share a common high-level structure (SQL, Python, data modeling, system design, behavioral) but the rubric weights and the specific test focuses differ significantly per company. A data engineer candidate who optimizes for one FAANG without understanding the others' specific bars often clears one loop and fails the next.
Meta (formerly Facebook) weights communication and trade-off articulation heavily. The SQL round tilts toward window functions and gap-and-island patterns for engagement streak detection. Presto and Trino dialect is what you see internally. Design rounds frequently center on the ads attribution pipeline (impressions, clicks, conversions with 28-day windows, multi-touch attribution) or the feed-ranking signals pipeline (10B+ events per day, single-digit-millisecond serving latency for the ML ranker). The behavioral round at Meta explicitly scores 'thinks out loud' and 'asks clarifying questions' as separate dimensions from technical correctness.
Amazon weights correctness and clean code heavily in the technical rounds, with Leadership Principles framing every behavioral and design answer. The stack is AWS-native: Redshift, Glue, EMR, Kinesis, S3, Athena. The bar-raiser round (an outside interviewer whose vote can veto a hire) is unique to Amazon and probes deeper on cultural fit. The data engineer SQL round tests Redshift-specific dialect (DISTKEY, SORTKEY, COPY, VACUUM).
Google weights complexity reasoning in the Python round more than other data engineer loops; Big-O articulation expected for every data structure choice. The stack is GCP-native: BigQuery, Dataflow, Pub/Sub, Dataproc. BigQuery's QUALIFY and ARRAY/STRUCT manipulation come up in the SQL round. Design rounds expect a GCP-native architecture with cost reasoning (BigQuery slot consumption, Dataflow worker hours).
Netflix runs Spark at extreme scale; the PySpark or Scala-Spark round is dedicated (45-60 minutes) and the SQL round includes Spark SQL questions. Iceberg as the table format is internal default; mention it in design rounds. Late-arriving data is a recurring theme; clients can be offline for days and events need to update past aggregates without overwriting.
Apple's data engineer interview loop varies more by team than the other FAANGs. The services and analytics teams tilt toward warehouse architecture (currently a mix of Hadoop/Hive legacy and modern Snowflake/Spark). The hardware teams tilt toward operational pipelines feeding manufacturing analytics. The bar is consistently high but the tested topics shift by team.
- How do FAANG data engineer loops differ from each other?
- Shared high-level structure (5 rounds: SQL, Python, modeling, design, behavioral) but different rubric weights. Meta weights communication. Amazon weights Leadership Principles and correctness. Google weights complexity reasoning in Python. Netflix weights Spark and late-arriving-data design. Apple varies by team. The dialect and stack assumed in interviews also differs per company.
- Should I prep for all 5 FAANG at once or one at a time?
- One at a time once you know your target. The foundational skills (SQL, Python, modeling) are 80 percent overlap; the per-company specifics (Presto syntax for Meta, BigQuery for Google, Leadership Principles for Amazon, Iceberg for Netflix) are the last-mile 20 percent that makes the difference between a strong loop and a no-hire. If you are early in prep, build the foundation; if you are 2-4 weeks from an onsite, focus on the specific company's bar.
- Which FAANG has the highest data engineer bar?
- Subjective and varies by team. Common takes: Netflix has the highest bar for Spark and large-scale design; Google has the highest bar for Python complexity reasoning; Meta has the highest bar for narration and communication; Amazon has the highest bar for cultural fit (via Leadership Principles and the bar-raiser round); Apple varies most by team. None is easy; all expect a candidate who can clear all 5 rounds.
- What is the FAANG-versus-non-FAANG difference for data engineer interviews?
- FAANG loops are typically more rigorous on scale (problems framed at 10B+ events per day, multi-region complexity), more rigorous on edge cases (multi-seed-grader-style fishing for NULL bugs, tie handling, idempotency), and more rigorous on behavioral rounds (specific cultural frameworks at Meta and Amazon, conversational depth at Netflix). Non-FAANG loops at strong companies (Stripe, Databricks, Snowflake, Airbnb, Uber) often have comparable technical bars but different cultural framings.
- What stack should I assume in data engineer design rounds at each FAANG?
- Meta: Presto/Trino plus Hive plus Spark plus internal tools. Amazon: AWS-native (Kinesis, Glue, EMR, S3, Athena, Redshift, DynamoDB). Google: GCP-native (Pub/Sub, Dataflow, BigQuery, Dataproc, Cloud Storage, Bigtable). Netflix: Spark plus Iceberg plus Mantis plus Kafka plus S3. Apple: varies by team; Snowflake and Spark common in newer teams, Hadoop/Hive legacy in older ones.
- How do I handle the cultural fit rounds at FAANG?
- Amazon: 5-7 STAR-D stories explicitly mapped to 2-3 Leadership Principles each. Meta: emphasis on communication and asking clarifying questions; less rigid framework. Google: Googleyness and Leadership themes (ownership, collaboration, ambiguity tolerance) without rigid framework. Netflix: 'stunning colleagues' bar; expect probing for ownership and the ability to disagree productively. Apple: varies; generally expect direct, technical, no-fluff answers.
- What level should I target if I have 5 years of experience as a data engineer?
- 5 years typically maps to L5/E5/Senior across FAANG. The L5 bar weights trade-off articulation, failure-mode naming (3 per component in design rounds), and mid-round adapt-on-fly. L4 (4-5 years floor) emphasizes clean foundational solutions; L6 (8-10+ years) emphasizes org-level design influence. Recruiters can sometimes flex you between L4 and L5 based on loop performance.
- What is the typical FAANG data engineer onsite duration?
- 4-5 hours for an onsite, 5-6 hours for senior+. 4-5 rounds of 45-60 minutes each, lunch interview (informal but observed), and sometimes a follow-up call for any unclear signals. Phone screen is typically 60-75 minutes covering SQL plus a behavioral or shortened design. Whole loop process: 4-8 weeks from first phone screen to offer, depending on company and team availability.
346 practice problems matching this filter. Domains: SQL (256), Data Modeling (12), Python (76), Pipeline Architecture (2). Difficulty: medium (146), easy (138), hard (62).
SQL (256)
- 10 Lowest Uptime Services - medium - Ten services at the bottom of the reliability chart.
- Proof of Presence - medium - Two-factor sent. How many confirmed?
- 30-Day Page View Counts - easy - Thirty days of engagement. Quick snapshot.
- 7-Day Token Retention - medium - Premium tokens, day by day.
- The Long Tail - medium - Averages hide the pain. The tail is where SLOs live.
- Active Users With April Transactions - easy - Active accounts that also opened their wallets. How many?
- Presence vs. Participation - medium - Being in the region and being active are two very different things.
- All Infra Regions - easy - The infrastructure spans the globe. Map it.
- The Tag Order - hard - Tags arrived in chaos. The system needs them in line.
- API Call Distribution Fraction - hard - Not all endpoints are created equal.
- Echo Chamber - medium - Same status, same pattern. Coincidence?
- Average Event Progression Time - hard - How fast do users move through the funnel?
- Average Review Comments by Author - medium - Some authors get more feedback than others.
- Average Session Duration - medium - How long do users actually stay?
- Average Session Duration by Device - easy - Session length, device by device.
- Average Sessions Per User - hard - How often do users come back?
- Kings of the Calendar - hard - Every month has a winner.
- The Heavy Hitters - easy - Total sales tell the real story. Surface the products that carry the catalog.
- Build Success Rate by Trigger - medium - Which triggers produce green builds?
- Build Success vs Failure by Repo - medium - Green versus red, repo by repo.
- Busy Authors - medium - Some developers spread their commits everywhere.
- The Notification That Paid Off - hard - The message went out to thousands. A smaller number actually bit.
- Losing Altitude - hard - Two months apart, the same campaigns. Find the ones that slipped.
- Campaign Revenue Totals - easy - Every campaign has a price tag. Total them up.
- CDN-Related DNS Lookups - easy - DNS lookups tied to the CDN.
- Character Position in Endpoint - easy - URL patterns, character by character.
- Cheapest CDN Route - easy - The cheapest path across regions.
- Cheapest Cost Per Region - easy - Lowest spend per region.
- Cheapest Transaction per User - easy - Everyone has a smallest purchase.
- The Quiet Outlier - hard - Ignore what the traffic does all day. Find the spike that barely showed up.
- Clicked Ad Impressions - easy - They saw the ad. They clicked.
- Loyalty's Double Tap - medium - When a nudge and a banner team up.
- Diminishing Returns - medium - When search comes back thin, does the click still follow?
- Cloud Cost Trend Analysis - medium - Cost trends across billing periods.
- Clean Exit - easy - Priority one. Only some of them made it to the end.
- The Blind Spot - medium - Pages they haven't discovered yet.
- Content Session Counts - medium - Session metrics, content item by item.
- Cost Share Within Category - medium - Each entry's slice of the category total.
- Service Roll Call - easy - The mesh is sprawling. Find out exactly how many services are actually running.
- Parallel Traces - medium - Same experiment. Different variants. Who overlaps?
- Live Wire - medium - The last switch each team flipped.
- Customer Full Name Concat - easy - First name, last name. Combine them.
- Custom Message Type Counts - medium - Not all messages are created equal.
- Daily Cross-Platform Users - easy - Mobile and web. Same day, same users?
- Daily Error Resolution Ratio - medium - Reported versus removed. The daily ratio.
- Daily Net Revenue - hard - Net revenue, day by day. Refunds included.
- Daily Session and User Counts - medium - Sessions and users, day by day.
- Campaign Click Rate - medium - Among engaged users, which campaigns landed.
- Days with More Edited Than Unedited Messages - medium - Some days, more messages get edited than sent.
- Department Cost by Status - medium - Headcount and compensation. The dashboard view.
- Deployments per Environment - medium - Dev, staging, prod. Where do most deploys land?
- Deploy Reliability Scores - medium - A reliability scoreboard for deploy teams.
- The Apprentices Still in the Forge - easy - A model is not a model until it stops learning and starts earning.
- Device Type Serving Most Users - medium - One device type serves more users than the rest.
- Device Types With Chrome Users - easy - Power users and their devices.
- Disabled-Flag Share by Owner - medium - Which teams ship everything off by default.
- Two-Way Street - medium - A conversation reads the same from either side.
- Distinct Product Categories - easy - A quick category inventory.
- Double Take - medium - Passed QA twice. That's the problem.
- Duplicated User Event Messages - medium - Duplicated messages from the alerts topic.
- Duplicate Training Runs - medium - Same model, trained twice.
- Verbose by Design - hard - Some paths say in six segments what others say in two.
- Engagement Gap - medium - Zero transactions is still a data point. Count everyone.
- Fault Lines - medium - Errors by day and region. Some areas are worse than they appear.
- Errors With Service Health - easy - Error data, enriched with health context.
- Even-ID February Signups - easy - A very specific slice of a very specific cohort.
- Even-ID June Signups - easy - Odd IDs, even IDs. The filter is precise.
- Event Count on Key Days - easy - Key days. Key event volumes.
- Events by Month Across Years - easy - Month by month, year by year. The pattern emerges.
- Event Types Spanning Multiple Months - easy - Some events span seasons.
- The Ones Worth Paging - hard - Three levels wake someone up at night. Tally each one.
- The A/B Verdict - medium - Variant A or Variant B. The conversion numbers pick the winner.
- Fastest Page View to Click - hard - How fast from view to click?
- Feature Flag Adoption - medium - How widely adopted are the flags?
- Feature Flag Fan vs Detractor Pairs - hard - Some users love the flag. Others want it gone.
- Feature Name Intersection - hard - Training names versus serving names. The overlap.
- Filtered User Roster - easy - A clean roster for the all-hands.
- Find Deploy Authors - easy - Same person. Many different spellings.
- Find the Fifth Largest Cost - medium - Not the biggest. Not the smallest. The fifth.
- The Ninety-Day Comeback - hard - Everyone shows up once. Who comes back before the quarter ends?
- First Half of Page Views - medium - Half the data. The first half.
- First Interaction Credit - hard - Attribute transactions to earliest touchpoint
- First Migration Record - easy - The very first migration. Where it all began.
- First Contact - easy - Every pipeline has a first run. This is what it brought back.
- Frequent Message Senders - medium - Someone is sending too many messages.
- Full Funnel - hard - Search. Browse. Buy. Only a few do all three.
- Health Checks per Service - easy - Some services get checked constantly.
- Heavy Hitters - medium - Some repos never sleep.
- Heavy Namespaces - medium - Kubernetes has favorites. Some namespaces carry more weight.
- High Engagement Pages - hard - Some pages hold attention longer than others.
- Highest and Lowest Cloud Costs - medium - The extremes in cloud spending.
- Highest Daily Spend - medium - Somewhere in that window, someone broke the spending record.
- High Price Products - easy - Everything above 100.
- High-Rated In-Stock Percentage - easy - Highly rated and in stock. A rare combo.
- Impressions by Search Keyword - hard - Campaign performance, keyword by keyword.
- Radio Silence - medium - Android control cohort. Gone quiet.
- Inactive Unverified Users - easy - Signed up. Never verified. Never came back.
- Inactive Users in Date Range - medium - Ghost accounts. Active signup, zero sessions.
- Intra-Region Latency Diff - hard - Same region. Different latency.
- The Upgrade Divide - medium - The install numbers don't match the hype.
- iOS Sessions by Device Type - medium - iOS engagement, device by device.
- Largest Group - easy - One group towers above the rest.
- Largest Single Cloud Cost - medium - One line item. The biggest bill of all.
- Last Five Batch Jobs - easy - The last five. A quick tail check.
- Last Migration Record - easy - The most recent migration. Is it the last?
- Latest Session Per User - easy - Everyone has a most recent session.
- Latest Version Per Service - easy - The latest version deployed. Each service.
- Longest Deploy With Full Identifier - easy - The longest deployment. Full ID.
- Long Searches Containing 'er' - easy - Long queries with 'er'. A pattern?
- Low-Volume Stream Topics - medium - Quiet topics in the stream.
- Max Value Per Location - easy - Every location has a peak.
- Mentorship User Pairs - medium - Pair them up. Mentor and mentee.
- Messages Containing Keyword - easy - Flagged terms in the messages.
- Messages From Specific Users - easy - Specific users. What did they say?
- Metric Range by Department - medium - Where each team's numbers sit, low to high.
- Mid-CPU Nodes - easy - Not the heaviest. Not the lightest. The middle.
- Mid-Range Cost Allocations - easy - Not the cheapest. Not the priciest. The middle.
- The Floor Price - medium - Before the negotiation, find what each provider really charges at its cheapest.
- Mobile Event Counts - easy - Mobile engagement, device by device.
- Then and Now - hard - Accuracy used to be higher.
- Creatures of Habit - hard - Every big team has one service it can't quit.
- Ebb and Flow - hard - The rise and fall of revenue, one month against the last.
- Most Common Monday Outcome - medium - Mondays have a pattern.
- The Fast Lane - medium - Every millisecond has to earn its keep.
- Most Efficient Region by Token Usage - hard - Some regions squeeze more out of every token.
- The Tiebreaker - easy - One column wasn't enough. The second column settles it.
- Multi-Host Regions by Node Type - medium - Some regions are quietly building empires.
- Multi-Variant Experiments - easy - One user, multiple experiments.
- Mutual Channel Connections - medium - Two users. What channels do they share?
- Never-Ordered Products - easy - In the catalog. Never purchased.
- Infant Mortality - hard - The youngest ones break first.
- Nodes by Region and Type - medium - Broken down by region. Broken down by type.
- Noisiest Tables by DQ Failures - medium - The tables that fail the most checks.
- Non-Draft Content - easy - Everything except drafts.
- Seen or Ignored - medium - A send is only half the story. Find where the taps actually land.
- Did Anyone Actually Read It? - easy - A push isn't a win until a thumb taps it.
- The Vanishing Rows - easy - Some records disappear when the tables meet. Figure out why.
- Oldest Alert per Service - hard - The oldest unresolved alert per service.
- When They Opened - medium - Push after push goes out. Some months, people actually read them.
- Peak Activity by Device - easy - Activity windows, device by device.
- The Heaviest Hitters - easy - A handful of impressions earn more than the rest combined. Bring them to the top.
- The Loudest Neighbor - hard - In every namespace, one pod does most of the eating.
- All at Once - hard - How many tokens were alive at the same time?
- Pipeline Completion Rate - medium - How far do users get through the flow?
- Power Users - medium - Engagement separates tourists from regulars.
- Power Users by Session Activity - medium - More sessions. More time. The power users.
- The Regulars - medium - Past a certain threshold, casual becomes committed.
- Priciest Item in Each Category - medium - The most expensive item per category.
- After the Cutoff - easy - The calendar turned but prod kept moving. See which services never stopped shipping.
- Product Name Letter Replace - easy - A quick text transform on product names.
- Product Name Prefix - easy - Just the first three characters. That is all.
- The Open Question - medium - Push sent. How many opened?
- The Notification Lifecycle - medium - Sent, opened, ignored. What happened after the alert went out?
- Q2 Search Volume - easy - Q2 search volume. The numbers.
- The Weight of the Cloud - medium - Not every dollar counts the same. Close the quarter.
- The Relentless Searchers - medium - Most users look once and leave. A few never stop looking.
- Once and Only Once - hard - Repeated readings are noise. The value seen a single time is the signal.
- Recurring Error Types - easy - The same errors, recurring.
- Regional Sales Growth QoQ - hard - Quarter-over-quarter growth. Region by region.
- Repeat Buyers Across Halves - medium - First half buyer. Second half buyer. Same person.
- Repeat Purchase Window - medium - The retention squad is looking for repeat purchasers.
- Resolved vs Unresolved Alerts - hard - Resolved versus open. By severity.
- Retargeting Campaign Impressions - easy - Retargeting impressions. All of them.
- Returning Buyers - medium - They came back and bought again.
- Two Names on the Ledger - easy - Two accounts. One ledger. Watch the spend stack up.
- Reviewer Performance Metrics - medium - Some reviewers are thorough. Others are fast.
- Many Eyes - medium - Every codebase draws its own circle of watchers. Count who shows up.
- Reviews Per Reviewer - easy - The workload split across reviewers.
- Rolling Revenue Average - hard - Smooth out the revenue bumps. The trend matters more.
- In the Shadow of the Peak - medium - Every provider has a top-line cost. Find the one right behind it.
- Search Algorithm Rating - hard - How good are the search results?
- Search Terms Starting With G - easy - Queries starting with 'g'.
- Second Highest Cloud Cost - medium - The second biggest bill on record.
- Where the Year Leans - medium - Some teams file early, some file late. See which way each one tips.
- Server With Most Errors - medium - One server stands out. Not in a good way.
- Services at Median Uptime - medium - Exactly at the median. Not above, not below.
- Service Scorecard - hard - Deploys vs. alerts. One row per service tells the whole story.
- Services With Multi-Quarter Uptime - hard - Multi-quarter uptime streaks.
- Session Count Distribution - hard - How are sessions distributed among the newest users?
- Session-Fit Content - easy - Content that fits the session length.
- Session Overview - medium - Full engagement picture, even for the ones who never showed up.
- Session Page View Distance - hard - Page view distance per session.
- Sessions Per Device Type - easy - Sessions, device by device.
- Shared Category Purchasers - medium - They bought different things from the same aisle.
- Shared Channel Contacts - hard - User networks mapped through messages.
- Shared Endpoints - medium - Shared credentials across endpoints.
- Signups by Age Bucket Since April - easy - Recent signups by age.
- The Compliance Order - easy - Token scopes need to be in the right sequence before the audit.
- The Middle Ground - medium - Strip the outliers from both ends. What does the core actually add up to?
- Symmetric Reply Network - medium - Every reply is an edge. Draw it both ways.
- Tables With Many DQ Failures - medium - Some tables have never once passed QA.
- Under the Line - medium - Twice the average is the ceiling. Find the teams comfortably beneath it.
- Double Vision - easy - Before the records move, the ones wearing the same name twice have to surface.
- The February Cohort - easy - One signup window. One cohort. Who joined the club?
- The Legacy Hunt - easy - Old data. Still matters.
- The Podium Finish - medium - Top two products per category.
- The Publishing Audit - easy - Published years ago. Still generating views?
- The Token Census - easy - How many tokens are out there?
- Third Highest Spender - medium - Bronze medal in spending.
- Third Largest Batch Job - easy - Bronze medal in the batch job rankings.
- Threads Excluding User - easy - Every thread they're not part of.
- Three Lowest Distinct Cloud Cost Amounts - easy - The three cheapest bills on record.
- Titles Ending With S - easy - Naming conventions. Specifically the plurals.
- Keys That Never Die - medium - Some API keys have no expiry date at all. That should worry someone.
- Top 10 CPU-Heavy Nodes - medium - The ten hungriest nodes.
- Top 10 Rated Products - medium - The ten highest-rated items.
- Top Active Senders per Channel - medium - Top three messages per channel by replies.
- Top Alert Resolvers - medium - The engineers who resolve the most.
- Top API Caller - medium - One user triggered more API calls than anyone.
- The Loudest Caller - easy - One account carries the traffic. Trace it back to everything it can touch.
- The Ones They Opened - medium - Every campaign fights for the tap. Find the ones beating the field.
- Top Category by User Segment - medium - Each segment has a favorite category.
- Top Chat Contributors - medium - The ten most active chat users.
- Top Cost Entry per Team - medium - The single biggest bill per team.
- Top Framework by Deployments - hard - The framework most often deployed.
- Top Identified Event Types - medium - The top users by events, but only the identifiable ones.
- Top Metric Values - easy - The five highest numbers. No duplicates.
- Competing Standards - hard - Every framework has a star model.
- Top Percentile API Tokens - hard - The most suspicious tokens.
- Top Services by Uptime - medium - Uptime is a competition. Which services never blink?
- Total Cost by Category - easy - Total spend per category.
- Total Hours Between Consecutive Events - hard - Hours between state changes.
- Total User Spend - easy - Each customer's total. Summarized.
- Only Here - hard - Exclusive to one source. Missing from the other.
- Transaction Overview - easy - The executive snapshot. Users, products, revenue.
- Deep Pockets - medium - One month, every customer, every dollar accounted for.
- Transaction Share of User Spend - medium - Each transaction's share of the whole.
- The Named Transaction - easy - Transaction IDs are useless without context. Bring in the product names.
- Trim Endpoints Right - easy - Trailing whitespace. Clean it up.
- Trim Search Terms Left - easy - Leading whitespace. Clean it up.
- Unclicked Searches by Campaign - medium - Searched but never clicked.
- Unique Hosts by Node Type - easy - How many unique hosts per node type?
- Unique Reporters per Content - medium - How many people flagged each item?
- Unique Searchers - easy - How many users actually searched?
- Who's Looking - easy - Every search is a question someone needed answered. Count the people asking.
- Unique Stream Topics - easy - A clean inventory of streaming topics.
- US-East KV Store Entries - easy - KV store inventory. us-east-1.
- User 360 - hard - One row per user. Everything they did, or didn't do.
- User Campaign Overlap Percentage - hard - How much ad overlap between users?
- Six Degrees - hard - Every reply ties two names together. Find whose web reaches the furthest.
- User Devices - medium - Desktop, mobile, tablet. What does each user actually use?
- User Engagement Summary - medium - Sessions plus searches. The full engagement picture.
- Behavioral Range - easy - Power users don't just visit more. They do more things.
- User Sessions on Specific Days - easy - One user. Specific days. What happened?
- Users Per Device Type - easy - Users per device. The split.
- Users Who Clicked Ads - easy - Ad clickers and their account details.
- Users Without Sessions - medium - Account created. Never logged in.
- Users With Purchase Events - easy - At least one purchase. That changes everything.
- Verify Commit ID Uniqueness - easy - Duplicate commit IDs. Are there any?
- View Count Per Page - easy - Every page has visitors. Some just have more.
- Point of Entry - hard - Everyone starts by looking. Count who came back to buy.
- Views by Content Type - medium - Count content views broken down by content type
- Weekly Build Status Report - hard - Every CI run, bucketed by week.
- Bookends of the Week - hard - Every week has two edges. Weigh what lands on each.
- Weekly Transaction Volume - easy - Weekly volume. The pulse.
- Word Count Per Message - medium - How wordy are the messages?
Data Modeling (12)
- A Number for the Seller - easy - They want a total. Give them the right schema first.
- Content Engagement Data Model - hard - Post published. Now measure everything that happens next.
- The Vanishing State - easy - A status column forgets the moment it changes. Model the schema that remembers.
- Food Truck Operations Data Model - medium - Mobile vendor, fixed menu, unpredictable locations.
- The Shape of a Run - medium - Two log lines bracket every process. Pair them and the fleet's rhythm appears.
- Marketplace Sales Warehouse - hard - No schema given. The interviewer is watching.
- The Last Mile - medium - Order placed. Now track it to the door.
- The Sales Architecture - medium - Numbers are easy. Making them queryable at scale is the real job.
- Two Wallets - medium - Two user types. Multiple payment methods. One messy billing table.
- The Churner Who Came Back - hard - They cancelled. They came back. The report has to tell both stories correctly.
- The Plan That Changed Twice This Month - medium - Subscribers come, go, downgrade, and share. The schema has to keep up.
- The Transfer Request - medium - Apply, wait, get approved or denied. Track all of it.
Python (76)
- Batch Records - medium - Too many at once. Break them into groups.
- Where the Line Breaks - easy - Every batch has a last piece. Mark it right.
- Column Max - easy - One value rules the column.
- Column Range - easy - From minimum to maximum. What is the spread?
- All Told - easy - Every shift leaves a number behind. Total the fleet.
- Cumulative Sum - medium - The total grows with every row.
- Corner to Corner - medium - A grid pressed flat still remembers its corners.
- Dictionary Key Intersection - medium - Two dictionaries. What do they share?
- The Carousel - medium - Every value takes its turn; the wheel keeps spinning.
- Even Filter - easy - Only the even ones survive.
- Explode List - easy - One row holds many values. Unpack it.
- Find Indices - medium - It is in there somewhere. Where exactly?
- All the Way Down - easy - Sections nested inside sections. One stream, in order.
- The Last Known Good - medium - When a column goes quiet, its last reading keeps standing.
- Greeting Formatter Class - easy - First impressions are formatted carefully.
- Null Counter - easy - How many holes in the data?
- Portfolio Profit Calculator - medium - Portfolio gain from purchase history and current prices.
- Quality Gate - easy - Not everything passes inspection.
- Quantile Calculator - easy - Mark the boundary value at a given point.
- Full Circle - medium - Load has to keep moving. Pass it down the line.
- Run Length Encoding - easy - AAABBB becomes 3A3B. Compress it.
- Sort Descending - easy - Biggest first. No exceptions.
- Subarray Signal - medium - One stretch carries the strongest signal.
- The Spike - hard - Spot the outliers before they page someone.
- Best in Class - medium - Every category keeps its leaders. The rest fall away.
- What the Night Changed - hard - Two photographs of the same table, a day apart. Account for everything that moved.
- The Squeeze - easy - Long stretches of sameness collapse into almost nothing.
- Downstream - hard - Nothing runs until what it needs has run.
- The Deep Config - medium - Every setting has a path. Trace it down to the value.
- The Deep Dive - easy - A specific position in the unsorted pile.
- The Dependency Resolver - medium - Everything depends on everything.
- The Mirror Index - easy - Every value remembers who pointed to it.
- The Dominant Signal - easy - Hottest items in the transaction log. Ties included.
- The Email Ranker - medium - Some inboxes see more action.
- The Firehose - medium - A stream arrives out of order. Roll it up, one time window at a time.
- The Event Bucketer - easy - Logs slotted into buckets.
- The Forward Fill - easy - Patch the gaps in a noisy sensor stream.
- The Gap Filler - easy - Fill the Nones with the last real value.
- The Generous Ones - medium - The generous ones are obvious.
- The Halftime Score - easy - Middle value of a dataset. No built-in shortcuts.
- The Horizon Scanner - medium - For each position, what is coming up ahead?
- The IP Validator - easy - Real and fake, mixed together.
- The Log Pulse - easy - Some lines repeat themselves.
- The Middle Ground - hard - The middle value keeps moving.
- The Nearest Value Mapper - medium - Close enough counts. Ties go low.
- The Numbered Chair - easy - A standing list. Position n holds one entry.
- The First of Their Kind - easy - When the same record arrives twice, only the first one survives.
- The One-Way Street - easy - The longest stretch that never turns around.
- The Original Keeper - easy - Clean up duplicate events without losing the timeline.
- The Output Peak - hard - One stretch outpaced all the others.
- All the Way Down - medium - The payload nests without end. The warehouse needs one flat row.
- The Pipeline Filter - easy - In the door as one thing, out the door as another.
- The Record Reconciler - medium - Two versions of the same truth.
- The Repeat Review - medium - The echo came back.
- The Resume Sifter - medium - Pull what's useful. Skip what you know.
- The Running Total - easy - Each position holds the sum of everything before it.
- The Schedule Cleaner - medium - Overlapping sessions. One clean line.
- What Changed Overnight - medium - Schema from yesterday vs today. Something changed.
- The Schema Migrator - hard - Old schema in, new schema out.
- The Sequel Spotter - easy - Spot the sequels hiding in the catalog.
- The Shifting Standard - medium - A benchmark in motion.
- The Social Graph - easy - Everyone knows someone.
- The Spin Doctor - medium - Ninety degrees, but which way?
- The Squeeze - easy - aaabbb gets old fast. Shrink it.
- The Streak Breaker - easy - It has a problem with repetition.
- The Stream Averager - easy - The answer moves with the data.
- The Stream Joiner - hard - Events don't wait for each other. This does.
- The String Shrinker - easy - Compress the string. Shorter wins.
- The Target Hunt - medium - Pairs that hit a target. Every one of them.
- The Throttle Ceiling - medium - Too many requests in too short a timeframe. Throttle it.
- The Throttle Wall - hard - Stop the abusers. Let the rest through.
- The Trade Signal - easy - Buy low, sell high. Identify the ideal moment.
- The Word Mismatch - easy - Some text does not match.
- Transform Column - easy - Same data, new shape.
- Against the Grain - medium - Turn the grid on its side and let every column stand up as a row.
- Value Count - easy - How many of each? Count them.
Pipeline Architecture (2)
- The Decision Before the Door Closes - hard - The window to stop it is smaller than you think.
- The What-If Machine - hard - A million slots. A thousand campaigns. Every combination matters.