Skills for Software Engineers

Not LeetCode grinding for its own sake — interviewers expect you to pick the right data structure for a problem in a real codebase (hash maps for O(1) lookup, trees for hierarchical data, graphs for relationship traversal). Missing this at interview time is a fast-fail.

Python alone no longer signals serious engineering depth at competitive companies. One statically-typed language on your resume — particularly one you can explain memory semantics for — substantially increases pass rates at L4+ screens.

Even backend engineers who say "I use an ORM" need to write raw SQL when diagnosing slow queries. Hiring managers increasingly include a "debug this slow query" problem in technical screens across all engineering roles.

Every team uses Git, but the gap between "knows git commit and push" and "knows how to manage a long-running feature branch, resolve complex merges, and write useful PR descriptions" is where junior engineers fail in collaborative environments.

The ability to design a clean REST API — with correct status codes, sensible resource naming, and a thought-out authentication flow — is a baseline screen at most mid-size and large companies. Bad API design cascades into client-side bugs.

You do not need to be a DevOps engineer, but understanding how to deploy a service, configure IAM permissions correctly, and use managed storage (S3/GCS, RDS/Cloud SQL) is now table-stakes for senior+ and expected even at mid-level.

The shift to "you build it, you run it" means engineers are expected to own the pipeline that takes code from a PR to production. Not setting one up from scratch, but understanding how tests run, how artifacts are built, and what a deploy gate is.

At mid-level+ in 2026, every company with a serious engineering bar includes a systems design round. The expectation is not Twitter-scale architecture but the ability to reason about tradeoffs: when to use a cache, how to handle auth, what breaks at 10x traffic.

Not 100% coverage dogma — practical test judgment. Knowing which logic to unit-test (branching, edge cases), which to integration-test (service seams), and when to skip (trivial getters, auto-generated code) differentiates senior candidates.

Building and running containers is now expected at most software engineering roles, even if Kubernetes is someone else's problem. The ability to write a functional Dockerfile and compose a multi-service local dev environment removes you from the "talks about Docker in theory" category.

On-call readiness is increasingly evaluated in interviews. Candidates who have worked with structured logs (JSON format, trace IDs), error aggregation (Sentry, Rollbar), and basic metrics (Prometheus, Datadog) are hired faster into senior roles.

The ability to design a schema that scales — not just one that works today — separates mid from senior. Understanding when to index, when NOT to index (write-heavy tables), and what N+1 queries look like in an ORM is the difference between a database that serves a million users and one that falls over at ten thousand.

The ability to write a design doc that gets cross-team buy-in — or a postmortem that surfaces root causes without blame — is the highest-leverage soft skill in engineering. It directly determines how fast you can build with others at scale.

Knowing that something is slow is not a skill. Knowing how to profile it, isolate the bottleneck, and make a targeted fix — without touching the unrelated code — is what separates engineers who keep production fast from ones who add caches hoping for the best.

In 2026, feature development increasingly includes an LLM-powered component — structured output extraction, retrieval-augmented generation, or AI-assisted user flows. Engineers who have shipped at least one real LLM integration are prioritized for greenfield projects.

The question is no longer whether to use AI coding tools — it is whether you use them as a crutch or as a force multiplier. Engineers who understand how to prompt effectively, when to reject completions, and how to maintain code quality with AI assistance ship measurably faster.

Edge runtimes (Cloudflare Workers, Vercel Edge, Deno Deploy) are moving compute closer to users and changing how teams think about latency and state. Not a hiring requirement yet at most companies — but relevant on any team building performance-critical web applications.

AI product teams are now building systems where multiple LLM calls chain together, tools are invoked autonomously, and outputs feed back into the loop. Understanding how to architect an agentic system — including failure modes, evaluation loops, and cost controls — is a differentiated skill entering most job descriptions at AI-forward companies.

The single most cited book in senior SWE interviews. Covers distributed systems, databases, stream processing, and consistency models with enough depth to survive follow-up questions. Not a quick read — work through it with a highlighter.

Best for: System design preparation; must-have skill tier.

The highest-signal list of 150 LeetCode problems organized by pattern (not difficulty or number). Each has a video walkthrough from the maintainer. The free tier covers 90% of what most companies test.

Best for: Data structures and algorithms; must-have skill tier.

Not an algorithms book or a framework tutorial — covers engineering judgment: DRY, orthogonality, tracer bullets, debugging mindsets. The concepts apply regardless of language and compound over a career.

Best for: Engineering practices and soft skills; nice-to-have tier.

Top-down teaching approach that gets you building real models before explaining theory. Legitimately free, taught by Jeremy Howard (Kaggle #1). Better for engineers who want practical ML exposure than academic ML courses.

Best for: Emerging ML/AI skills; emerging tier.

The best way to learn cloud is to use cloud with real dollars at stake — even if minimal. Build a project using EC2, RDS, S3, and IAM roles from scratch without a tutorial holding your hand. The friction of making mistakes is the learning.

Best for: Cloud fundamentals; must-have tier.

Anthropic publishes unusually detailed guidance on structured output, system prompts, and multi-turn patterns — more engineering-focused than OpenAI's equivalent. Required reading before integrating Claude into a production application.

Best for: LLM API integration; emerging tier.

Most engineers have superficial Git knowledge. GitHub's own documentation covers branching models, rebase strategies, and conflict resolution with diagrams. Supplement with "Pro Git" (free online) for the internals.

Best for: Git workflows; must-have tier.

Teaches a vocabulary and pattern set for making code incrementally better without breaking it. Essential reading before any senior code review round — candidates who have internalized refactoring patterns write better PR feedback.

Best for: Code quality and engineering practices; nice-to-have tier.

The canonical text on observability, reliability, and incident management at scale. Not just for SREs — any engineer who wants to understand production operations at depth should read Part II (Principles) and Part III (Practices).

Best for: Observability and reliability; nice-to-have tier.

Not for general advice (lots of noise), but for company-specific interview patterns. Search "[Company] interview 2025" to find recent documented loops. Cross-reference across threads to identify signal vs. noise.

Best for: Interview preparation across all tiers.