Built for Data & AI Career Growth
Everything you need to master data and AI skills, ace interviews, and land your dream role at top tech companies.
Our Mission
To be the most complete platform for data and AI career growth — covering the skills, questions, projects, and learning paths that take you from curious beginner to confident professional, whether you are headed toward Data Science, Machine Learning, or AI Engineering.
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Interview Questions
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Companies Covered
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Members Worldwide
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Data & AI Projects
Built by Data Scientists for Data & AI Professionals
StrataScratch started in 2017 as a small side project from founder Nathan Rosidi, who was not only a working data scientist but also a professor — teaching the next generation of data professionals while seeing firsthand how disconnected most interview prep resources were from the job itself. Most taught generic coding puzzles, not the real, messy, business-driven SQL and Python questions actually asked in data interviews.
Nathan didn't build it alone. Sergey Parkhomenko — a developer by training who had already worked alongside Nathan on a previous startup — joined from the beginning days to turn the idea into a real platform, building the systems that let candidates practice with real questions, sourced from actual interview loops at companies like Amazon, Meta, and Google, and write and run code directly in the browser.
What began as a tool for a handful of early users has grown into a platform trusted by data and AI professionals worldwide, expanding from coding questions into full learning paths, data projects, and career tools covering the fast-growing AI Engineering, MLOps, and Applied Science tracks alongside its Data Science roots. Nathan continues to build the platform according to the vision and needs of the industry, with Sergey serving as CTO. Both are supported by a team of experienced data and AI professionals — data scientists, AI engineers, ML engineers, applied scientists, and data engineers — who help keep StrataScratch true to its mission: preparing people for what's real in the industry, not just what's easy to test.
2017
Founded
2
Co-Founders
SF, CA
Headquarters
“I've watched candidates ace generic puzzles, then freeze on a real interview question or a messy, business-driven data project — because that's not what they'd practiced with. We built StrataScratch on real questions and real projects, sourced from actual interview loops. AI has only raised the stakes: it's not enough to get the right answer anymore, you have to be able to defend it — whether you're a Data Scientist explaining a model, or an AI Engineer defending a RAG pipeline's design.”
The Most Complete Platform for Data & AI Career Growth
We believe preparing for a data or AI career shouldn't mean piecing together scattered SQL tutorials, outdated courses, and disconnected practice problems. It should be one place that grows with you — from your first SELECT statement, to building and evaluating your first model, to the interview that gets you hired, to the ongoing projects that keep your skills sharp in an ever-changing industry.
Real questions, not guesswork
Every question is grounded in what companies actually ask — for Data Science and AI roles alike.
One platform, every stage
From foundational learning paths to advanced ML and AI engineering portfolio projects.
Built with the community
Shaped by feedback from the 500K+ members who use it.
Always current
Content updated as companies, tools, and interview formats evolve — including the rapid shift toward AI-native roles.
What We Stand For
These are the principles that guide what we build and how we support the people using StrataScratch.
Learner-First
Every feature starts with a simple question: does this actually help someone get better or get hired? If it doesn’t serve the learner, we don’t build it.
Real-World Relevance
We source questions and projects from real interviews and real take-home assignments — across Data Science and AI Engineering roles alike.
Continuous Innovation
From AI-powered code validation to new project formats, we keep pushing the platform forward as the data and AI field evolves.
Community-Driven
Our 500K+ members shape the platform through feedback, discussion, and shared solutions — learning together, not alone.
Ahead of an AI-Driven Industry
AI is reshaping interviews and day-to-day data work, so we’re continually adding AI Engineer, MLOps, and Applied Scientist content to match — and building AI into our own toolkit to give real-time feedback that mirrors the modern interview.
Companies We Follow
We track the data and AI roles, hiring trends, and technical standards at the world's top companies — so our content always reflects what the industry actually demands.
Technology
Amazon, Apple, Asana, Box, Cisco Systems, Dropbox, Google, IBM, Microsoft, Nvidia, Oracle, Ring Central, Salesforce, Shopify, Tesla, Twitter
Social Media & Entertainment
ESPN, Meta, Netflix, Pinterest, Spotify, TikTok, Twitch
Fintech & Financial Services
American Express, Block, Bloomberg, Credit Karma, PayPal, Robinhood, Stripe, Visa
E-Commerce & Marketplace
Airbnb, DoorDash, Instacart, Lyft, Postmates, Uber, Whole Foods Market
Real Estate & Local
City of Los Angeles, City of San Francisco, Redfin, Zillow
Healthcare & Wellness
CVS Health, HealthTap, Noom
Business Intelligence & Data
Crunchbase, Forbes, Glassdoor, Wine Magazine, Yelp
Consulting & Professional Services
Accenture, Deloitte, Tata Consultancy
Travel & Transportation
Delta Airlines, Virgin
Cloud & Infrastructure
Job Positions We Follow
We track the specific Data and AI roles top companies hire for — mapping each position to the skills, question types, and difficulty levels that show up in real interviews.
Data Analyst
Easy – MediumCore skills tested
SQL (aggregations, joins, filtering), Python or R, data visualization, business acumen, product sense
Question types
Reporting queries, metric calculations, cohort analysis, funnel analysis, A/B test interpretation
Amazon · Meta · Google · Airbnb · Spotify · Microsoft · Salesforce
Data Scientist
Medium – HardCore skills tested
SQL, Python (Pandas, NumPy, Scikit-learn), statistics, probability, A/B testing, ML fundamentals, product sense
Question types
Statistical inference, experiment design, predictive modeling, feature engineering, business case studies
Meta · Google · Amazon · Airbnb · Lyft · Netflix · Microsoft
Machine Learning Engineer
Medium – HardCore skills tested
SQL (window functions, joins, deduplication, cohorts), Python, PySpark, dataset construction, label creation, leakage prevention
Question types
Dataset construction for model training, time-aware feature engineering, churn/fraud/conversion label building
Amazon · Meta · Google · Microsoft · Uber · DoorDash
AI Engineer
Medium – HardCore skills tested
Python, LLM APIs, prompt engineering, RAG architecture, vector databases, SQL, applied system design
Question types
RAG pipeline design, prompt evaluation, agent tool-use debugging, LLM output evaluation, latency/cost tradeoffs
Google · Meta · Microsoft · Amazon · Nvidia · OpenAI
Applied Scientist (AI/ML)
HardCore skills tested
Python, statistics, experiment design, ML fundamentals, model evaluation rigor, research-to-production translation
Question types
Model evaluation design, experiment design, research-to-production case studies, tradeoff defense
Amazon · Meta · Microsoft · Google
MLOps Engineer
Medium – HardCore skills tested
CI/CD for ML, model monitoring, feature stores, data/model versioning, cloud infrastructure, containerization
Question types
Pipeline design, deployment strategy, drift detection, rollback and versioning scenarios, infra tradeoffs
Amazon · Google · Microsoft · Uber · Netflix
Research Engineer
HardCore skills tested
Python, deep learning frameworks, paper implementation, algorithm design, distributed training, experimentation
Question types
Architecture implementation, algorithmic reasoning, reproducing research results, optimization tradeoffs
Google · Meta · Microsoft · Nvidia
Analytics Engineer
Medium – HardCore skills tested
Advanced SQL, data modeling, dbt, dimensional modeling, data warehousing concepts, Python basics
Question types
Data transformation logic, schema design, slowly changing dimensions, pipeline debugging, metric layer design
Airbnb · Stripe · Shopify · LinkedIn · Salesforce
Data Engineer
Medium – HardCore skills tested
SQL, Python, distributed computing (PySpark), ETL design, data modeling, database internals, cloud infrastructure
Question types
Pipeline design, batch/streaming trade-offs, schema migrations, performance optimization, distributed system design
Amazon · Google · Meta · Microsoft · Uber · LinkedIn
BI Analyst
Easy – MediumCore skills tested
SQL, data visualization (Tableau, Looker, Power BI), business metrics, KPI definition, stakeholder communication
Question types
Reporting queries, KPI calculations, trend analysis, cohort comparisons, dashboard logic
ESPN · City of SF · City of LA · Forbes · Yelp · Glassdoor
Product Analyst
MediumCore skills tested
SQL, A/B testing, funnel analysis, retention analysis, product sense, metric definition, experiment design
Question types
Retention queries, funnel drop-off analysis, feature usage metrics, experiment result interpretation, product case studies
Airbnb · Meta · DoorDash · Lyft · Instacart · Spotify
Software Engineer
Medium – HardCore skills tested
Data structures & algorithms, system design, SQL fundamentals, Python/Java/C++, API design
Question types
Algorithmic coding challenges, system design, data pipeline integration questions
Google · Amazon · Meta · Microsoft · Apple
Financial Analyst
Easy – MediumCore skills tested
SQL, financial modeling, forecasting, variance analysis, business acumen
Question types
Revenue/cost analysis queries, forecasting scenarios, KPI calculation, budget variance case studies
American Express · Bloomberg · PayPal · Visa
Skill Sets We Cover
Every skill set on StrataScratch is mapped to real interview requirements — across Data Science and AI Engineering alike.
SQL
From basic SELECT statements to advanced window functions, CTEs, and multi-table joins. The #1 skill tested in data and AI interviews.
Python
Data manipulation, analysis, and ML workflows. Covers Pandas, NumPy, Scikit-learn, and PySpark for big data environments.
Statistics & Probability
Hypothesis testing, distributions, confidence intervals, p-values, and Bayesian reasoning — core to data science and applied AI interviews.
Machine Learning
Supervised and unsupervised learning, model evaluation, feature engineering, dataset construction, and deployment concepts.
AI & LLM Engineering
Prompt engineering, RAG architecture, vector databases, LLM output evaluation, and agent design for AI Engineer and Applied Scientist interviews.
System & Data Design
Data pipeline architecture, schema design, ETL patterns, data warehouse modeling, and system design for data- and AI-heavy applications.
Career Paths
Whether you're breaking into data, leveling up, or pivoting into an AI-specialized role — StrataScratch maps a path for every stage.
Breaking Into Data
Build foundational SQL and Python skills through structured learning paths. Practice easy-to-medium questions and work toward your first data role.
Leveling Up Your Skills
Advance from analyst to scientist or engineer. Tackle medium-to-hard problems and build a portfolio that gets you noticed.
Targeting Senior Roles
Prepare for hard-difficulty rounds, system design questions, and advanced ML topics asked at top-tier companies.
Pivoting Into Data or AI
Coming from software engineering, finance, or another field? Use company-specific question sets to bridge the gap quickly.
Deepening a Specialization
Go deep on machine learning, data engineering, or product analytics with curated question sets and data projects.
AI & ML Career Path
One of the hottest paths in tech. Covers the SQL foundations, Python ML workflows, and applied AI skills that AI Engineers, MLOps Engineers, Applied Scientists, and Research Engineers need to pass interviews.
Our Content Catalog
A comprehensive set of question types, projects, and concepts — organized to reflect real-world Data and AI job requirements across 57+ companies.
1,000+
Coding Questions
Real SQL and Python interview questions from Amazon, Meta, Google, Airbnb, and 50+ other companies.
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Non-Coding Concept Questions
Statistics, probability, modeling, system design, product sense, business cases, and behavioral rounds.
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Data & AI Projects
Real take-home assignments. Projects cover ML, NLP, forecasting, applied AI, and analytics.
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Algorithm Questions
Sorting, searching, dynamic programming, graph traversal, and optimization.
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Visualization Questions
Charts, dashboards, and visual analyses with Matplotlib, Plotly, and Folium.
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Free Questions
Start practicing without a subscription — no credit card required.
What Our Members Say
Join 500,000+ data and AI professionals who have used StrataScratch to land jobs at top tech companies.
“StrataScratch is hands down the best platform if you are preparing for or am working as a data analyst/scientist/engineer. Happy to share that I have gotten an offer from Amazon as a Data Analyst.”
Triman
Data Analyst @ Amazon
“StrataScratch has been super helpful. I started with barely being able to finish an easy SQL query to no problem solving most of the medium level in just a few days.”
Kyle
@ Twitch
“StrataScratch and the community really helped me prep for the interview — I ended up getting the Data Scientist internship!”
Joe
Data Scientist @ Microsoft
“I really wanted to get into data analytics and data science but never really understood the importance of SQL. The questions are really very realistic and I was asked similar questions in my coding rounds.”
Hemant R.
@ Credit Karma
“SQL is something that is super important and plays a key role in Data Analysis. I used to practice 2–3 SQL problems everyday. The discussion forum and solution with clear understanding cleared my barriers.”
Vikram
@ Microsoft
“StrataScratch is awesome! It’s the first place I found that allows you to use Python in addition to SQL. The Facebook questions were quite similar to the ones that I saw in my interview today.”
Ryan
@ Meta
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