Big Tech platforms Software Engineer Interview Loop
The software engineer interview is the most documented one in the industry, and the data roles get measured against it. There's a recruiter call, then a phone screen with one or two medium data structures and algorithms problems. Then come four to six 45-minute rounds: two or three coding, one system design from mid-level up, and one behavioral. Meta's loop doesn't depend on the team, and it has added an AI-assisted coding round. Google decides by hiring committee, then matches you to a team. Amazon gives each interviewer Leadership Principles to cover and adds a Bar Raiser. Microsoft brings in a senior "As Appropriate" interviewer. This page is the data-adjacent version, for engineers joining data platform, infrastructure or analytics-facing teams.
Practice by round
Coding basics at speed, and the SQL that data-adjacent teams screen on. That means joins, aggregations and filters, written cleanly in a plain editor.
Medium and hard coding, like topological sort, binary-tree traversal, the longest path in a DAG, bipartite-graph detection and nested JSON flattening. There's also the harder SQL platform teams ask, with window functions, ranking and time between events. Talk through your approach before you type. State the complexity when you finish, and catch your own bugs.
| Question | Difficulty |
|---|---|
| Balancing Special Text | Hard |
| Nested JSON Flattening | Hard |
| Longest Path in DAG | Hard |
| Maximum Path-Sum in Tree | Hard |
| Binary Tree Traversal | Hard |
| Graph Topological Sort | Hard |
| Linked List Stack | Hard |
| Largest Perimeter | Hard |
System design on the companies' own features: Friends You May Know, a restaurant recommendation, a database for customer addresses, feed ranking, and two engines on one workload. Cover scale, storage and failure handling. Start with requirements and load, sketch the simplest thing that works, then name what breaks first. The projects let you practice building it end to end.
| Case study | Difficulty |
|---|---|
| AI Customer Service Quality Metrics | Medium |
| Location-Based Recommendation System | Medium |
| AI-Driven Comment Engagement | Medium |
| Short-Form Video Recommendation System | Hard |
| Feed Ranking Impact Experiment | Hard |
The basics that come up in follow-ups: tries and binary trees, linked lists versus arrays, generators, joins and CTEs. Teams close to AI now add LLM architecture questions too, like attention complexity, the KV cache, and encoder versus decoder.