All my experience is with the job market in Seattle. A decade ago I took a job at 80k (via craigslist), against competing offers around 95k, because it involved NLP and Hadoop. A year later I got a 40% raise to a Series E startup that ultimately flamed out, working on ML-adjacent big data stuff. I slowly bumped along until, with an average job tenure of ~2 years, until in the past four years I went from ~160k to slightly over 300k. Technologies have been Hadoop/Spark/Kubernetes ecosystems. First two job hops were via LinkedIn, next two were personal referrals.
The two books I think had the most impact were Mining Massive Datasets by Ullman et al, and Data-Intensive Text Processing by Lin & Dyer, but neither is very cutting-edge now.
I've never worked at a FAANG, but I've had a ~40% interview success rate with them.
My advice would be:
- If you're early in your career or want to switch subspecialties for higher potential remuneration, be willing to take a slight pay cut or work for a slightly shady/low-prestige company for the right experience.
- Actively maintain a detailed profile on LinkedIn. It's a shitty site/company, but that's where the recruiters are. Another tip I've used to get interviews is to do a search for recruiters at a company of interest, and just visit all their profiles. They'll get the notification, and if your profile has the right keyword bait, they'll reach out. Probably more effective than cold submitting a resume through their website.
- Be really lucky. There seems to be a huge amount of variability in terms of how well interview questions match your experience/competencies, as well as being lucky in picking sub-specialties in technologies that end up booming. Also I'd say my personal productivity across all my jobs had probably a 3x variation, so be lucky in that wherever you end up working happens to match your personal strengths and weaknesses. Productivity (and promotions/experience to showcase in interviews) is not just a function of the developer, it's a function of the developer plus the working environment.
A final comment I'll make is that I have a STEM BS, but it's not in CS. Instead I gave myself a virtual CS degree by working through textbooks over 3-4 years, covering the entire course curriculum of a CS undergrad. I'm probably lucky in being temperamentally suited to completing that sort of project.
The two books I think had the most impact were Mining Massive Datasets by Ullman et al, and Data-Intensive Text Processing by Lin & Dyer, but neither is very cutting-edge now.
I've never worked at a FAANG, but I've had a ~40% interview success rate with them.
My advice would be:
- If you're early in your career or want to switch subspecialties for higher potential remuneration, be willing to take a slight pay cut or work for a slightly shady/low-prestige company for the right experience.
- Actively maintain a detailed profile on LinkedIn. It's a shitty site/company, but that's where the recruiters are. Another tip I've used to get interviews is to do a search for recruiters at a company of interest, and just visit all their profiles. They'll get the notification, and if your profile has the right keyword bait, they'll reach out. Probably more effective than cold submitting a resume through their website.
- Be really lucky. There seems to be a huge amount of variability in terms of how well interview questions match your experience/competencies, as well as being lucky in picking sub-specialties in technologies that end up booming. Also I'd say my personal productivity across all my jobs had probably a 3x variation, so be lucky in that wherever you end up working happens to match your personal strengths and weaknesses. Productivity (and promotions/experience to showcase in interviews) is not just a function of the developer, it's a function of the developer plus the working environment.
A final comment I'll make is that I have a STEM BS, but it's not in CS. Instead I gave myself a virtual CS degree by working through textbooks over 3-4 years, covering the entire course curriculum of a CS undergrad. I'm probably lucky in being temperamentally suited to completing that sort of project.