As I mentally ready myself to start my new role as a Data Analyst next week, I’ve been looking back at how I got here. It took over two years from the moment I really decided to make a career switch, which was a lot longer than I originally planned. I don't regret the decision, but I’ll admit I was both overly optimistic and a bit short-sighted. After all, making a major career pivot in my 40s after a year-plus work gap was never going to be an easy feat.
It’s also an interesting (and perhaps slightly risky) time to become a data analyst. AI is reshaping the role, automating routine junior engineering, analyst, and scientist tasks while forcing everyone to work differently. Some people may have already figured out what working "differently" looks like. For the rest of us, including myself, it’s still an open question. And honestly, whatever answer I come up today might become obsolete in six months.
So, what does this all mean? I figured this journey might offer something useful, or at least relatable, to others attempting a mid-career pivot or striving to break into data. I don't intend for this to be a guide, but rather a single data point, a neighbor’s work diary to share notes, compare struggles, and hopefully offer a bit of encouragement.
The real fun starts next week! But as a prelude, I want to share the story of how I got to this starting line, covering my journey up to mid-2025. To keep things structured, I’ve set this up as a Q&A with a virtual interviewer.
Q: Why did you want to switch careers from Product Manager to Data Analyst?
A: I wanted to be more hands-on with data. I really love data, and I wanted analyzing (and building) to be my core focus rather than a secondary responsibility. I actually enjoyed being a Product Manager and managing product strategy and roadmaps (albeit mostly for internal tools in my previous roles). However, as you climb the ladder in product management, you naturally move further away from hands-on execution, and I missed that satisfaction.
Q: When and how did you start the journey?
A: Late 2023 was when I made the firm decision to move forward. I had stepped away from my previous job in mid-2023 to manage family business and also fight my own depression. I started by completing online certifications in data analytics and data science. I wanted to see where I was and show prospective employers that I was serious about the transition.
Q: What were those certifications, and how were they?
A: Programs like the Google Advanced Data Analytics Specialization and Tableau Certified Data Analyst. Coming from a Computer Science background and having worked in technical roles, the course content was fairly straightforward. You can check out the full list on my LinkedIn profile.
Q: Were those certifications helpful?
A: Maybe? Probably not for directly landing a job offer 🙂 But they were good to study and likely played a role in getting accepted into a master's program later on.
Q: What else did you do besides certifications?
A: Like everyone else breaking into the field, I built portfolio projects. I’ve since pruned some of the earlier ones, but a few remain on my portfolio site. I removed the simpler projects because AI coding assistants have made that level of work trivial; what felt like a solid demonstration a couple of years ago is now a mere baseline.
Q: You started in late 2023. How did 2024 turn out?
A: I did not start actively applying until the second half of 2024, as I needed time for family matters and portfolio building. I started to really look for new job opportunities from latter half of 2024, but that didn't really go anywhere. I posted publicly on Linkedin that I am looking for job, being optimistic in my message, but nothing materialized. I've probably sent few dozens of tailored resumes and coverletters, but I none of them wne into interviews. I sent out dozens of tailored resumes and cover letters, but none converted into interviews. I reached out to former colleagues and data analysts for referrals and spoke with a few recruiters, but I couldn't pass the initial screenings. I don't think I performed poorly, but I simply wasn't competitive or compelling enough relative to the rest of the applicant pool.
Q: So what did you do?
A: Two data analyst ex-colleagues gave me a similar answer. Without a relevant Master's degree, many hiring teams wouldn't even consider a resume in this market. I had hoped my CS degree and past experience at big tech companies like Google and Yahoo would give me enough leverage, but as I mentioned, I was being overly optimistic.
So, I applied to several Master's programs in related fields. After a few attempts, I was accepted into UC Berkeley's Master of Information and Data Science (MIDS) program and enrolled in May 2025.
Q: What did you not do?
A: Two main things:
I didn't network effectively. If I had networked more aggressively, maybe I would have landed a role sooner or skipped the Master's program altogether. I actually enjoy deep conversations and listening to people, but traditional social networking events and drinking culture just aren't my thing.
I didn't exaggerate or lie. I might have shortened the timeline if I had forged my resume, but I wasn't willing to do that. Part of me wonders if I just wasn't "desperate" enough to take shortcuts, and that's something that still stays in the back of my mind.
In my next entry, I’ll dive into how I finally secured interviews and landed the offer. Till next time!