Crack the SQL Round Like a Pro: What Top Candidates Do Differently
Want to level up your data skills with SQL? This article breaks down exactly what to practice, how to think, and what interviewers look for.
👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and the practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.
SQL is the one skill every data science interview at big tech includes—and it’s often your first hurdle. Whether you're applying to Google, Meta, Netflix, Amazon, or any fast-scaling startup, expect at least one (often more) SQL rounds.
Here’s how to set yourself up for success, especially if you're aiming for roles that demand strong analytical rigor and product thinking.
Understand What’s Actually Tested
SQL interviews aren’t just about syntax correctness. What’s really being assessed:
Business comprehension: Can you translate ambiguous, messy product questions into structured queries?
Data intuition: Do you understand what tables are involved, what keys to join on, and how to filter cleanly?
Clean logic: Can you structure your query in a modular, readable way using CTEs, subqueries, or window functions?
Optimization: Will your query run on a large dataset without blowing up memory?
Example prompt from a Netflix interview:
“We want to understand the drop-off rate in episode completion. Given a views table with user_id, episode_id, and duration_watched, write a query that calculates the percentage of users who watched more than 80% of an episode.”
Top candidates clarify assumptions (e.g., "Is episode duration provided or do I infer 100%?"), plan their logic before coding, and handle edge cases cleanly.
Master These Core Topics
Here's what actually shows up again and again in SQL interviews:
✅ JOINs
You must be comfortable joining multiple tables, even self-joins.
Know when to use INNER JOIN vs LEFT JOIN.
Understand join keys: composite, surrogate, or natural keys.
Check out this newsletter on mastering SQL Joins
Tip: Practice debugging join errors (e.g., duplicate rows from many-to-many joins).
✅ Window Functions
Crucial for ranking, running totals, time comparisons, or de-duplication.
ROW_NUMBER(), RANK(), DENSE_RANK() – for filtering top-N per group
LAG(), LEAD() – for churn/retention analysis
SUM() OVER(PARTITION BY …) – for user-level aggregations
✅ Subqueries & CTEs
You should be able to nest logic cleanly, use multiple CTEs in a stack, and understand scope.
Prefer readable, layered CTEs over deeply nested subqueries.
✅ Date & Time Logic
Filtering by date ranges, using DATE_TRUNC, and calculating rolling windows is essential.
✅ Aggregations & Filtering
Using GROUP BY, HAVING, COUNT(DISTINCT) with precision.
Avoiding common mistakes like filtering post-aggregation incorrectly.
✅ Conditional Logic
CASE WHEN statements are key to cohorting users, segmenting by behavior, or bucketing metrics.
Check out this detailed newsletter on Optimizing SQL Queries with CASE WHEN technique.
3. Practice Like It’s Game Day
Reading solutions ≠ solving problems.
The best prep is doing, not consuming. Set a timer. Use an SQL IDE. Treat each question like it’s a live interview.
Try this: Do 5 questions/day for 2 weeks, and review every error you make—even small ones.
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4. Review Real Interview Questions
Patterns emerge once you study actual company questions. You'll notice:
Amazon loves questions tied to order fulfillment and customer behavior.
Google emphasizes logical flow and user interaction analysis.
Meta likes exploratory queries with product thinking.
Where to find them:
Glassdoor: Search “Data Scientist SQL questions” for each company.
Interview Query: Categorized by role and company.
Prepfully: Has anonymized candidate transcripts for data roles.
Pro tip: Build a personal “SQL question log” where you capture:
The business context
Query solution
Mistakes you made and learned from
5. Understand Query Execution (Advanced, but Powerful)
Most candidates focus only on writing correct SQL.
But at most companies, especially at senior levels, interviewers love candidates who demonstrate awareness of query performance and execution behavior.
SQL queries aren’t executed top-down. The actual logical processing order is:
If you're asked to “optimize” a query or asked “How would this scale to millions of rows?”, a good answer might be:
“I’d check the execution plan to ensure it’s not doing full table scans, and that join keys are indexed. I’d also consider whether a window function is more efficient than a subquery.”
Correctness gets you through the door. Performance thinking sets you apart.
Even a basic understanding of query execution logic can make your answers feel more thoughtful, especially in senior data science roles. Check out this newsletter on the SQL Query Execution Order.
6. Go Beyond the Code
This is what separates top 5% candidates:
They narrate their thought process as they go.
They clarify assumptions before coding (“Are there duplicate users per order?”).
They can explain alternative solutions and trade-offs (e.g., CTE vs temp table, window function vs self join).
Even if your final query has a small bug, showing your logic clearly earns you points.
Practice with a peer or record yourself explaining your solution aloud.
Final Thought
SQL might seem like the easy part—but it’s often the reason candidates don’t move forward. The best data scientists treat SQL interviews like business problems, not just code challenges. Understand the “why,” structure your logic, write clean code, and explain clearly.
You’ll stand out.
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