Data Engineer Job Simulation
Northwind's finance team stopped trusting the revenue dashboard, and they were right to. Four separate bugs in one nightly pipeline: a join that fans out, an inner join that drops rows, a load that double-counts on retry, and a WHERE clause that returns nothing at all.
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Ruth CallowayData Engineering Lead, Northwind Logistics
Welcome to Northwind. I'm Ruth, and I lead data engineering here. We move freight, and every morning our finance team opens a revenue dashboard - except two weeks ago they stopped, because the numbers were wrong. Your first ticket is to find out why. I'll warn you now: it is not one bug, it is four, and one of them is subtle enough that most engineers miss it even after finding the other three. Your second ticket is to design the replacement. I care most about the last requirement on that list - why nobody noticed for two weeks.
Your manager for this simulation. Reviews feel this real too.
Why finish this simulation
A risk-free way to experience the data engineering job before you're hired - debug a real pipeline whose numbers finance can check, design a warehouse model under a real time budget, and back your applications with evidence rather than a list of tools.
SQL judgement, not SQL trivia. Nobody asks you to write a window function from memory. They ask why the revenue doubled - and that is a join fan-out you have to see in the numbers.
The bug that hides behind three others. Four defects, and the NOT IN with a NULL is the one most engineers miss even after finding the rest. Once seen, never missed again.
Idempotency where it actually bites. A retried Airflow task that doubles a day's revenue is the clearest possible lesson in why loads must be safe to run twice.
Modelling under real constraints. 400 million rows, a 6-hour rebuild and a 4-hour window. Designing inside that is the job; designing for an ideal warehouse is not.
Data quality as engineering. The last requirement is that a human should never again be the detection mechanism. That reframes quality from a dashboard into a pipeline gate.
Feedback that names the gap. Every submission returns per-question results and a rubric read of your notes, so a retry is targeted rather than a guess.
How it works
01Pick up a ticket
Real tasks land on your board like a Jira queue. Download the dataset and work in your tool of choice.
02Submit for review
Your work enters review - results within 24 hours (usually much sooner). The clock pauses while you wait, like a real take-home.
03Pass, then progress
Pass to unlock the next ticket. Clear every ticket to earn a certificate and a spot on the weekly leaderboard.
Skills you will learn and practice
- Spotting a join fan-out from the numbers alone
- Why COUNT(*) lies after a one-to-many join
- Inner vs left join, and the rows that silently vanish
- NULL semantics: why NOT IN returns nothing
- Idempotent loads and why retries double your revenue
- Rolling reprocess windows for late-arriving data
- Slowly changing dimensions for historical accuracy
- Reconciliation checks that block a bad publish
The tickets
What you'll learn
- How a one-to-many join silently doubles an aggregate
- Why COUNT(*) after a join counts the wrong thing
- Why NOT IN against a NULL returns nothing at all
What you'll do
- Read an Airflow task, its SQL, the schema and two weeks of run history
- Attribute four separate finance-visible symptoms to four separate causes
- Write the review that goes to the finance lead who stopped trusting the table
Weekly leaderboard
Highest ranking points top the board - your score counts 80%, your speed 20%.
Your Future Certificate
Preview only. Clear both tickets to earn your personalized certificate - free.
Verified Certificate
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Performance Analysis
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Issued by DPIIT-recognized & MSME-registered company
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