Tech jobs: what companies actually want (and how you get one)

Want a tech job but feel lost by all the noise? Companies hire people who solve real problems and show they can ship work. That means hands-on projects, clean code habits, and clear communication — not just certificates.

Common roles hiring right now: software developer (web/mobile), data scientist, machine learning engineer, DevOps/SRE, QA/automation, network engineer, and product/technical roles. Each role shares core skills: programming, problem solving, and the ability to explain what you built. Knowing which skills map to which role helps you target your learning and applications.

Skills that actually get you hired

Start with a small, practical list and get very good at it. For most entry and mid-level tech jobs this includes: Python or JavaScript, SQL, Git, basics of Linux, and one cloud (AWS/GCP/Azure). Add Docker and basic Kubernetes for operations roles. For AI/data roles learn pandas, scikit-learn, and an ML framework like PyTorch or TensorFlow. For web work learn HTML/CSS and a framework like React or Node.js.

Also build debugging and testing habits: write unit tests, use a debugger, and practice reading stack traces. Employers notice candidates who write clear README files, document APIs, and automate repetitive tasks. Soft skills matter: clear writing, reliable communication, and the ability to take feedback speed up hiring.

Action plan: 8 steps to move from learning to hired

1) Pick a role and narrow your stack. Don’t learn everything at once. Pick 3 core techs and master them. 2) Build three concrete projects: a deployed web app, an API with tests, and a small data/ML pipeline or automation script. Host code on GitHub and include a clear README and a short demo video or GIF.

3) Practice coding problems: aim for 100 solved LeetCode problems covering arrays, strings, trees, and hashing for interviews. 4) Prepare two system-design sketches for mid-level roles: APIs, scaling basics, and simple caching patterns. 5) Tailor your resume to each job: list measurable results, not just tasks. Replace “worked on feature” with “reduced load time by 30%” or “automated a 4-hour manual task to run in 5 minutes.”

6) Mock interviews and feedback. Use peers or tools to simulate coding and behavioral rounds. 7) Network: reach out on LinkedIn with a short message about a specific project or question — don’t send generic requests. Apply to jobs daily and track applications so you follow up promptly. 8) Negotiate: ask for a salary range early, compare with local market data, and be ready to explain your unique value with examples from your projects.

One fast tip about AI: it’s changing workflows, not replacing fundamentals. Learn how AI tools can speed up your work (prompt engineering, using APIs, automating tests) and add a basic ML understanding if you want to work on AI teams.

Start small, ship projects, and keep learning. If you follow the steps above you’ll move from studying to getting interviews and landing a tech job faster than you think.