How to Prep for a $150K+ SQL Technical Interview — A Step-by-Step Guide

There's a specific kind of frustration that comes with reaching the SQL technical round of a senior data role, knowing you've written SQL professionally for years, and still walking out feeling like you under-performed. It's the most common interview heartbreak in this market.

The good news: it's almost always a preparation problem, not a skill problem. Senior data engineers and analysts who land $150K+ roles aren't necessarily better at SQL than the candidates who don't. They've just prepared in a more structured way for what these interviews actually test, which is a different thing than what you do at work day-to-day.

This is the four-week plan that's worked for the candidates I've coached through Meta, Google, Amazon, Stripe, Snowflake, and a handful of AI startups paying mid-$100Ks to mid-$200Ks. Steal the parts that fit your timeline and gap.

Who This Guide Is For

This isn't a beginner's tutorial. This is for you if:

• You write SQL at work, can usually get the answer, but feel slow under interview pressure
• You've been rejected from a senior data role in the last six months and want to know why
• You're considering applying to FAANG, an AI lab, or a high-paying SaaS company and want to actually be ready

The roles this guide targets pay $150K–$280K total comp at the analyst, data scientist, and data engineer levels, mostly at companies with rigorous data interview loops. The same prep transfers up to staff-level roles ($250K–$400K+) with one extra dimension: system design. We'll touch on that briefly at the end.

The Honest Diagnosis: Why You Fail Even When You Know SQL

Every candidate I've coached has a different gap, but they cluster into four buckets:

1. You can write SQL but can't explain it. You can solve the problem and still bomb the interview because you went silent while typing.
2. You know the patterns but they take you 40 minutes. Interviewers have a 25-minute window. Speed isn't optional.
3. You over-engineer. You reach for window functions when a GROUP BY would do it. Senior interviewers downgrade for unnecessary complexity.
4. You don't ask clarifying questions. Diving straight in reads as junior. Senior signal is pausing first.

If you don't know which bucket is yours, that's Week 1's homework.

The Four-Week Plan

This assumes 8-10 hours per week — about an hour a day plus a longer weekend session. You can compress to two weeks if you do more daily, but four is the durable cadence.

Week 1: Foundations Audit

The week most candidates skip. Don't.

Day 1–2: Take a baseline timed test. Sit down with five SQL problems of escalating difficulty. Time-box yourself to 20 minutes per problem. Don't peek at solutions. Don't use Stack Overflow. Just write SQL and submit it mentally as if you were interviewing.

Write down for each:

• Did you finish in time?
• Was your query correct on every edge case, or just correct on the happy path?
• Could a teammate read it without asking what each block does?

This is your honest baseline. Most people score lower than they expect. That's the point of this exercise — you can't fix a gap you can't see.

Day 3–4: Patch the gaps you found. Common ones:

• Window functions feel uncomfortable → spend 90 minutes drilling ROW_NUMBER, RANK, LAG, LEAD, and SUM() OVER (PARTITION BY ...). These five cover 80% of what FAANG asks.
• CTEs feel verbose → practice rewriting nested subqueries as CTEs. You'll start to feel when one is cleaner than the other.
• Joins still trip you up on edge cases → write five queries deliberately using each join type (INNER, LEFT, RIGHT, FULL OUTER, CROSS) and explain the difference out loud.

Day 5–7: Read three real interview transcripts. Find write-ups from people who've done the loop at your target companies. Pay attention not to what they wrote, but how they narrated it. The phrasing is the skill you're learning here.

Week 2: Pattern Mastery

This is the week you become fast.

Most SQL interview questions reduce to one of seven patterns. Memorize them and your time-to-first-correct-query collapses by 30%.

The Seven Patterns:

1. Aggregation with grouping. GROUP BY with one or more dimensions. Plus the edge cases — what about empty groups, what about NULLs in the grouping column.

2. Top-N per group. Use ROW_NUMBER() OVER (PARTITION BY group_col ORDER BY metric DESC) and filter to rn = N. This is the most-asked window pattern at FAANG.

3. Funnel / sequential events. Pivot events per user using MAX(CASE WHEN event = 'X' THEN 1 ELSE 0 END) then compute step-to-step conversion rates.

4. Cohort retention. Define cohort, define retention event, bucket the gap, aggregate. Same shape every time.

5. Running totals / cumulative. SUM(metric) OVER (PARTITION BY group ORDER BY date) for cumulative or ORDER BY date ROWS BETWEEN N PRECEDING AND CURRENT ROW for rolling.

6. Deduplication. ROW_NUMBER() OVER (PARTITION BY natural_key ORDER BY recency_col DESC) = 1. Amazon loves this.

7. Comparing periods. Self-join or window function across date ranges to compare metric across two periods (YoY, MoM, WoW).

For each pattern: practice three problems. By the end of the week you should be able to identify the pattern within 30 seconds of reading the question and reach for the right SQL skeleton instinctively.

Week 3: Domain Practice

This is where most candidates fall down. They've mastered the SQL patterns on toy schemas, then walk into a FAANG interview where the schema is events, users, sessions, payments, and they freeze. The patterns are the same. The business context isn't.

The five domains you should practice:

• Growth analytics — user signups, activation, retention, churn. Pattern: cohort analysis on a users and events table.
• SaaS / subscription — MRR, expansion, contraction, churn movements. Pattern: pivoting movement events to compute net new MRR.
• Marketing — campaign attribution, cost per lead, email engagement. Pattern: join campaigns to leads to conversions across time windows.
• Product — feature adoption, A/B test analysis, NPS over cohorts. Pattern: difference-of-means with significance, conditional aggregations.
• Finance / FP&A — revenue recognition, expense vs budget, headcount cost. Pattern: window functions over financial periods with rollup.

Don't practice all five if you're interviewing at one company. Practice the two that match your target. Growth and Product are the most common at consumer / SaaS companies. SaaS and Finance show up at B2B / enterprise companies.

The trap: most online SQL practice doesn't use realistic business schemas. They use Employees and Departments tables, which doesn't simulate what FAANG actually puts in front of you. Find a practice surface with real-feeling domains.

Week 4: Live Simulation

The last week is the hardest because it requires another human or a convincing simulation of one. The goal: get used to performing under pressure while narrating.

Three things to drill:

1. Talk-while-typing. Solve five problems where you must narrate every decision out loud, even if alone. Record yourself. Listen back. If you went silent for 30+ seconds at any point, that's the failure mode the interviewer will see.

2. Time-box without warning. Have a friend or a timer interrupt you at 22 minutes and ask, "What would you do next if you only had three minutes?" This builds the muscle of triaging — knowing when to ship a working-but-imperfect query vs. continuing to optimize.

3. The follow-up storm. After you solve a problem, have your friend ask three follow-ups: "What if the data was 100x bigger?" / "What if user_id was nullable?" / "How would you verify this query is correct?" Senior interviewers always ask follow-ups, and your composure on these often matters more than the original answer.

The Top Five Mistakes That Cost Offers

After watching dozens of mock loops, these show up over and over.

Diving in cold. Sub-90-second silence between question and typing. Pause, restate the question, name your assumptions. This 60-second move alone has flipped more interview outcomes than any other single change.

Silent debugging. When the query errors, candidates often go quiet and stare. Narrate instead: "The error is ambiguous column reference — I need to alias the joined table." That turns a stumble into composure.

Over-engineering the first draft. A WITH block doesn't make a query better, just longer to verify. Write the simplest correct query first. Refactor only if it actually helps.

Skipping the edge-case call-out. Always say one edge case out loud, unprompted. "This wouldn't catch users with zero orders — do you want them in the result?" Senior signal.

Failing the follow-up. You wrote the query, it works, you exhale — then the interviewer asks "how would you test this?" and you go blank. Always have an answer: "I'd verify by spot-checking a few rows manually and by comparing the total count to a known baseline."

The Quiet Bar Above $200K

If you're targeting roles above $200K — Staff Analyst, Senior Data Scientist, Data Engineer L5+ — there's an additional dimension: design. Interviewers will ask you to design a schema, choose between a denormalized and normalized layout, or describe how you'd structure an ETL pipeline.

You don't need to be an architect to clear this bar. You need to be able to reason about tradeoffs out loud — denormalized is faster to query but harder to keep consistent; normalized is the inverse. Read one good chapter on data warehouse modeling (Kimball's star schema is the canonical introduction) and you'll be 80% there.

What To Practice Where

Honest answer: most practice platforms optimize for LeetCode-style puzzles, which is exactly the wrong surface for senior interviews. Senior interviews test business judgment on realistic schemas, not puzzle-solving on toy ones.

Pick a practice platform that gives you:

• Real business domains (Growth, SaaS, Marketing, Product, Finance) — not Employees/Departments
• Daily practice cadence to build the speed muscle
• Some form of AI or human feedback on your queries, not just pass/fail tests
• Realistic data with the messiness you'd see in production

If you're prepping for a FAANG or AI startup loop in the next four weeks, the single highest-ROI practice is doing one timed challenge every day on real business data, with AI feedback on what you could've done better. That's what builds the speed and judgment muscle interviewers are testing for.

The Mindset Shift

The reframe that makes the biggest difference: stop thinking of the SQL interview as a test and start thinking of it as a conversation about how you think. Interviewers aren't checking whether you know JOIN syntax. They're checking whether they'd want to be on a call with you debugging a real production query.

That reframe changes everything. You'd narrate. You'd ask. You'd call out the edge case. You'd say "I'm not sure — let me think out loud." That's the senior register, and once you internalize it, the SQL part takes care of itself.

Four weeks. Honest baseline, pattern mastery, domain practice, live simulation. That's the plan. Start today.