Beyond StrataScratch — Why the Top SQL Prep Sites Still Miss Senior Interviews

StrataScratch has earned its reputation. It's better than LeetCode for SQL — more realistic-feeling problems, longer questions, more "this could happen at a real company" framing. If you've outgrown LeetCode, StrataScratch is a natural step up.

But if you're prepping for a senior data role at FAANG, an AI lab, or a growth-stage SaaS company in 2026 — and you've been grinding StrataScratch — you may have run into the same wall many candidates hit. The questions feel real. The interviews feel different anyway.

This is a candid look at what StrataScratch trains well, where it still falls short of senior interview reality, and what to add to your prep to close the gap.

What StrataScratch Does Better Than LeetCode

Worth saying upfront: StrataScratch is a meaningful improvement.

• Real company branding. Questions are tagged with "Amazon", "Meta", "Microsoft." That alone shifts your mindset from "puzzle" to "interview."
• Longer, more business-flavored prompts. You're not just asked to find the second-highest salary. You're asked to compute a metric in a way that mirrors how an analyst would actually frame it.
• Multi-table schemas. Most StrataScratch problems give you 2-4 tables, which forces you to join thoughtfully — a step up from LeetCode's mostly single-table problems.

These improvements aren't trivial. A candidate who's mastered StrataScratch is genuinely more interview-ready than one who's only done LeetCode. The remaining gap is narrower — but the gap that remains is exactly the thing that separates "passes the SQL screen" from "passes the loop."

The Three Things StrataScratch Still Misses

1. The reasoning is still pre-decided for you

A typical StrataScratch prompt:

"For each user, find the most recent transaction. Return the user_id, the transaction amount, and the transaction date."

Notice what's already been done for you: the metric is defined ("most recent transaction"), the output shape is specified, the scope is fixed (per user). You just have to write the SQL.

A real senior interview prompt at, say, Stripe:

"Stripe's revenue team is trying to understand whether high-value merchants are at risk of churn. How would you approach that question?"

The metric isn't defined. The scope isn't fixed. The output shape is open. The whole reason the senior interview is senior is that it tests whether you can do the work before the SQL — which StrataScratch problems still pre-do for you.

2. The interpretation step is missing

StrataScratch tells you if your query is right by checking it against expected output. The interview's hardest moment comes after your query is right, when the interviewer says: "OK, your number is 28.4%. What does that mean for the business?"

That's not a SQL question. It's an analysis question. And it's the single most common moment where candidates with strong technical skills lose to candidates with strong communication skills.

You can't practice this on a platform that grades pass/fail. You need a surface that asks you to interpret — to say what the result means and what you'd do next.

3. The questions don't follow up on you

A real senior interview is a conversation. You write a query. The interviewer says: "What if the data was 100x bigger?" Or "What if user_id was nullable?" Or "How would you verify this is correct without re-running it?"

Each follow-up tests a different muscle: query performance, edge case awareness, validation strategy. StrataScratch doesn't ask follow-ups. You write SQL, you submit, you move on. The follow-up muscle never develops.

This is the most under-discussed gap. Candidates who've drilled 200 StrataScratch problems will still freeze when a senior interviewer probes their answer.

A Concrete Example

Let's take a near-identical question and look at the two experiences.

The StrataScratch version:

"For each user, return the total revenue from their orders in the last 90 days, broken down by product category. Return user_id, category, and total_revenue."

You write:

SELECT
  o.user_id,
  p.category,
  SUM(o.amount) AS total_revenue
FROM orders o
JOIN products p ON p.product_id = o.product_id
WHERE o.order_date >= CURRENT_DATE - INTERVAL '90 days'
  AND o.status = 'completed'
GROUP BY o.user_id, p.category;

Pass. Move on.

The real senior interview version, same data:

"We want to understand cross-category purchase behavior. What would you investigate?"

You'd need to do five things StrataScratch never asks:

1. Define what "cross-category behavior" means. Is it "users who purchase across multiple categories" or "the rate at which a purchase in category A predicts a future purchase in category B" or "which category pairs co-occur most often in single baskets"?

2. Pick a metric. Each definition implies a different SQL.

3. Write the query.

4. Interpret the result. "Users in Electronics buy from Books at 18%, but Books → Electronics is only 4%. That asymmetry suggests Electronics is a 'gateway' category."

5. Propose what's next. "I'd want to see whether the asymmetry holds up by segment — is this driven by holiday gift-givers, or is it consistent year-round?"

Steps 1, 2, 4, and 5 are entirely absent from StrataScratch practice. Step 3 is the only one you've drilled.

What To Add To StrataScratch Prep

If StrataScratch has been working for you on the SQL fluency dimension, keep using it. But add three habits to round out the senior-level muscles:

1. Reframe every StrataScratch problem as ambiguous.

Take a StrataScratch problem. Read only the first sentence. Stop. Write down: what would I clarify before writing SQL? Then read the full problem. See how many of your clarifying questions the problem actually addresses. The ones it doesn't address are the senior-interview muscles you need to build.

2. After every solved problem, write the "next analysis" paragraph.

Don't move on. Write 3-4 sentences on what you'd investigate next, what hypothesis you'd test, what segment you'd slice on. This is the muscle senior interviews score.

3. Practice on schemas with realistic mess.

StrataScratch data is cleaner than real production data. Find a practice surface with NULLs, partial duplicates, soft-deleted rows, out-of-order events. The "spotting the data quality issue unprompted" skill is worth 1-2 levels of seniority by itself.

The Honest Bar

After watching dozens of candidates go through senior data interview loops in the last year, the pattern is clear: StrataScratch is necessary but not sufficient.

The candidates who get FAANG and AI lab offers in 2026 do three things StrataScratch alone won't train:

1. They handle ambiguity (StrataScratch removes it)
2. They interpret results (StrataScratch grades pass/fail)
3. They handle follow-ups (StrataScratch doesn't ask any)

To close that gap, you need a different kind of practice surface. One that gives you open-ended business prompts on real-feeling data, asks you to interpret results rather than just check them, and forces you to handle follow-ups instead of moving on after submission.

That's the prep surface senior interviews actually look like. The faster you start practicing on something that resembles the interview itself, the faster you'll close the gap StrataScratch leaves behind.