Big Tech platforms Analytics Engineer Interview Loop
An analytics engineer sits between the data engineer and the analyst, and Big Tech analytics engineer interviews borrow from both. There's a recruiter or hiring-manager screen and a SQL screen. Then come three to five rounds: advanced SQL, data modeling, pipeline and tooling questions, and stakeholder or behavioral. There's no statistics round. The concept questions here are about data rather than inference.
Practice by round
SQL basics at speed: joins, aggregations, distinct counts and date functions on event and order tables. Explain your join path in one sentence and move on.
Advanced SQL: window functions, CTEs, ranking, rolling and cumulative metrics, deduplication by key and timestamp, and top-N with ties. Then come the questions that run through it. What breaks in your metric when the source changes? What would you precompute for a report, and what would you calculate at query time?
| Question | Difficulty |
|---|---|
| Email Details Based On Sends | Medium |
| Liked' Posts | Medium |
| Find the number of customers without an order | Medium |
| Find the percentage of shipable orders | Medium |
| Number Of Custom Email Labels | Medium |
| Spam Posts | Medium |
| Number of Speakers By Language | Medium |
| MacBook Pro Events | Medium |
| Apple Product Counts | Medium |
| No Order Customers | Medium |
| Workers With The Highest And Lowest Salaries | Medium |
| Product Transaction Count | Medium |
| Acceptance Rate By Date | Medium |
| Clicked Vs Non-Clicked Search Results | Medium |
| SMS Confirmations From Users | Medium |
Database concepts and end-to-end projects. The concept questions get asked out loud in these rounds: UNION versus UNION ALL, when a view saves or costs storage, how an engine runs the query you just wrote, and what normalizing buys you. Then come the projects and mini-analyses, which are the center of the round. Raw tables go in, a metric you can trust comes out, and you have to defend the transformation logic.
| Case study | Difficulty |
|---|---|
| Linked list and Array | Medium |
| Database Normalization | Medium |
| Trie and Binary Tree | Medium |
| Data project | Difficulty |
|---|---|
| Image Files Processing | Easy |
| Image to Pencil Sketch | Easy |
| Daily Top Users | Easy |
| Top Departments by Headcount | Easy |
| Detecting Latency Outliers | Medium |
| Plan Payback and LTV | Medium |
| Resolving Duplicate Transactions | Medium |