Writing on Career growth
Articles on Career growth from Tauseef Fayyaz: practical writing on engineering, AI and tech careers, drawn from leading a team and mentoring engineers.
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17 posts tagged “career growth” · page 1 of 2
Twelve Moves That Take You From Mid Level to Senior
Getting to mid level is mostly time and repetition, and then the path stops being clear. The thing that got you here, doing assigned work well, is not what the next level rewards, and nobody says so directly, so capable engineers respond by doing more of what already worked. Twelve heuristics for the part that is not obvious.
The Habits That Make an Engineer Worth Routing Work Through
Every team has one or two engineers other people route work through, and what they have in common is not raw technical strength. It is that when something is given to them it comes back, and when it will not, you hear about it early. Fifteen habits that build that reputation, none of which require a title or permission.
Fourteen Questions Worth Answering Before You Change Jobs
The typical job change starts with one specific bad month, and within a fortnight the applications are out. Sometimes that is right, and often it produces a lateral move into a different set of problems discovered around month four. Fourteen heuristics for the whole arc: deciding, choosing, not being talked into or out of it, and leaving well.
A Job Search Is a Pipeline, Not a Performance
Most people run a job search as a series of hopeful individual events, which fails for a structural reason: the response rate on cold applications is low enough that any single one is close to noise. The people who find work reliably are running a pipeline instead. Thirteen heuristics for the mechanics, which are the part you actually control.
Choosing a Data Structure Is Choosing What to Make Cheap
Most people learn data structures as a list of things to implement, forget nearly all of it, and go back to using an array for everything. Implementation is the least transferable part: you will implement a hash map approximately never and choose between one and something else roughly every second week. Twelve pairings for making that choice.
What to Do When You Are Stuck Learning to Code
There has never been more good material for learning to program and the failure rate has not improved, which tells you the bottleneck is not access to explanation. It is that watching someone else solve a problem feels exactly like learning and is not. Sixteen heuristics for the first year, mostly about what to do at the point where you are stuck.
The Defaults I Would Learn First as a Software Engineer
Every roadmap for learning software engineering is the same technology list in a different order, and it tells you nothing about what to do when you are sitting in front of an unclear requirement on a Wednesday afternoon. Fourteen situations you will keep meeting, paired with the move that is usually right, from prototyping a vague requirement to blaming the system rather than the person.
Engineering Newsletters and Blogs Worth Your Inbox
Most engineers subscribe to fifteen newsletters, read none, and feel vaguely behind, because a full inbox produces guilt rather than learning. A shorter list sorted by what you would actually read each one for, why company engineering blogs beat most newsletters, and the weekly routine that makes any of it stick.
Ten Books That Fill the Gap a CS Degree Leaves
You can finish a computer science degree without reading a word of Kleppmann, Feathers, Fowler or Brooks, and leave knowing algorithms but not how software is actually built, debugged and maintained by teams. Ten books grouped by the problem each one solves, with the thing that matters most: when in your career each will actually land.
GitHub Repositories Worth More Than a Star
Everyone has forty starred repositories and has opened three of them. Twenty three worth keeping, grouped by what each is actually for: system design, things to build from, things to practise against, computer science foundations and things to look up, each with a note on how to use it properly and the three habits that decide whether any of it turns into learning.
An AI Engineering Roadmap That Puts Things in the Right Order
Every AI engineering roadmap is the same twenty topics rearranged, and the arrangement is the only part that matters. Six stages from model basics through retrieval, measurement, tool calling and agents, each with something specific to build, what to skip entirely, and the two stages people skip and later regret.
A Learning Roadmap for System Design
System design is the subject people put off longest, because every resource assumes you already know the vocabulary. A four stage sequence that fixes the order: vocabulary, then data, then services talking to each other, then real systems, with how long each stage takes, how to know you are done with it, and the mistake of collecting concepts instead of using them.
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