SQL · Python · Data Systems

Data Engineering Interview Lab

Solve SQL and Python tasks. Diagnose pipeline failures. Design reliable data systems.

Data Engineer path

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25 exercises

Daily revenue from changing delivery events
Deduplicate updates before calculating daily revenue.SQL
Users active on seven consecutive days
Find streaks without counting repeated events as extra days.SQL
Flatten nested source records
Convert nested dictionaries into safe dotted paths.Python
Repair a replayed incremental load
Keep the newest version, including deletion tombstones.Python
Booking metrics with changing property details
Choose table grain and preserve the history behind a metric.Data modeling
Delivery analytics with late and repeated events
Serve live operations and corrected finance reports from one event history.System design
A Spark join has one task that never finishes
Use task evidence to distinguish skew from insufficient compute.Debugging
Migrate a live reporting dataset
Connect a consistent snapshot to ongoing changes without losing records.System design
Action counts without duplicate joins
Join event tables without multiplying the result.SQL
Estimate time spent on each page
Use the next event while acknowledging what the logs cannot tell you.SQL
Prices that rise every month
Check strict growth and calendar continuity together.SQL
A checkout funnel in the right order
Count users who actually progressed through the stages.SQL
Accounts that disagree after migration
Find missing, extra, and changed records even when counts match.SQL
Top visitors with tied activity counts
Rank distinct-page counts while preserving ties.SQL
Median values by region
Group records and handle odd and even sample sizes explicitly.Python
Distinct accounts in time ranges
Apply half-open interval boundaries without double-counting activity.Python
Reconcile account balances across formats
Compare normalized records, not aggregate totals.Python
Separate valid events from bad input
Validate an ingestion batch without losing the evidence for rejected rows.Python
A revenue view doubles its totals
Find the table-grain defect before changing SUM.Debugging
A successful backfill writes today’s partition
Separate the processing interval from the wall clock.Debugging
A Spark job reads thousands of tiny files
Distinguish file listing and task overhead from a hot key.Debugging
A warehouse for orders, returns, and refunds
Use separate fact grains for events that can happen more than once.Data modeling
Consistent ratings from multiple providers
Resolve identity and rating scales without silently merging different items.System design
Fresh dashboards without rebuilding everything
Choose materializations from query patterns, correction rules, and refresh cost.System design
Repair a Booking Ledger and Occupancy Report
Repair a versioned booking ledger and its nightly occupancy report.Python

Data Engineering Interview Lab

Daily revenue from changing delivery events

Users active on seven consecutive days

Flatten nested source records

Repair a replayed incremental load

Booking metrics with changing property details

Delivery analytics with late and repeated events

A Spark join has one task that never finishes

Migrate a live reporting dataset

Action counts without duplicate joins

Estimate time spent on each page

Prices that rise every month

A checkout funnel in the right order

Accounts that disagree after migration

Top visitors with tied activity counts

Median values by region

Distinct accounts in time ranges

Reconcile account balances across formats

Separate valid events from bad input

A revenue view doubles its totals

A successful backfill writes today’s partition

A Spark job reads thousands of tiny files

A warehouse for orders, returns, and refunds

Consistent ratings from multiple providers

Fresh dashboards without rebuilding everything

Repair a Booking Ledger and Occupancy Report