Job Titles Won’t Save Your Tech Career — Skills Will

Tech job titles can look precise, but the same title often means completely different work. In this tech career advice video, I explain why chasing job titles can leave you confused — and why durable skills, real problems and practical projects are a better foundation for long-term career growth. I share two very different Data Scientist interviews from my own experience: one focused heavily on SQL, while the other centred on deep learning and model architecture. The label was identical, but the jobs were not. You’ll also see how infrastructure roles evolved from System Administrator to DevOps, SRE, Platform Engineering, MLOps and AI Infrastructure — while the underlying foundations stayed familiar: Linux, automation, networks, reliability and resource management. The same pattern appears across data and AI careers. BI Analyst, Data Analyst, Data Scientist, Data Engineer, ML Engineer and AI Engineer are not meaningless titles, but they are company-specific labels rather than universal definitions. The better questions are: • What problems do I want to solve? • Which skills will help me solve them? When you organise your learning around problems, every project compounds. Python can lead to machine learning; machine learning can lead to deployment; deployment can lead to Docker and Kubernetes. The title usually catches up after the capability is already there. If you’re changing careers, learning tech or deciding between data, AI, software and cloud roles, this video will help you build a clearer path without waiting for a job title to validate your progress. WATCH NEXT ▶ How to Learn Tech Skills Faster:    • How to Learn Tech Skills Faster (The Compo...   ▶ My Career Change Story — Geoscientist to AI Engineer:    • Geoscientist to AI Engineer: My Career Cha...   ▶ Subscribe for practical tech and career lessons:    / @promisekeh   What problem are you learning to solve right now? Share it in the comments. #TechCareers #CareerAdvice #AIJobs