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Playbooks and hiring-loop notes for international interview practice
Avito’s 2026 engineering loop: HR screen, Go and distributed systems, practical algorithms, listing-search system design, and a final culture round. A two-week plan plus mock interviews and quizzes on the gym.
The three pillars of promotion and stories from Amazon, Meta, and Microsoft: readiness, perception, and when to speak up about your career.
A Go theory cheat sheet: goroutines vs threads, buffered channels, the G-M-P scheduler, concurrent GC, slices vs arrays, maps, strings, interfaces, defer, data races, context, and runtime init. Tables, pitfalls, 30-second answers.
Master 12 core System Design interview topics: replication, sharding, databases, indexes, proxies, load balancing, distributed transactions, outbox/inbox, backups, partitioning, replication modes, and delivery guarantees.
A 2026 career comparison: Data Engineer vs Data Scientist across work, stack, salaries, degrees, specializations, and market growth. Tables, workflows, a decision framework — then practice the answer on a mock.
The six theory prompts that show up beside coding tasks: sorting families, graph shortest paths, dynamic arrays vs lists, balanced BSTs, complete trees, and hash collisions. Trade-offs, follow-ups, and a complexity cheat sheet.
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A 2026 prep map: what changed, real prompt families, a five-step 45-minute loop, week plans — then timed quizzes and a pair mock so the list is not memorization.
Some loops hand you an assistant, others disqualify you for opening one. Which companies do what, how the AI-enabled round is scored, and a drill for both.
A 2026 reading list for system design interviews: Kleppmann, Xu, Newman, Huyen — then quizzes, a pair mock, and Gym courses so the books do not stay theory.
A 2026 guide to the data scientist role: daily work, vs analyst and engineer, Python/SQL/ML, pay bands, and a skill map you can practice on a mock.
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