Latest / The Comeback Career Podcast with Fexingo: Returning After a Gap, Stay-at-Home Re-Entry / How a Career Gap Helped One Parent Return as a Data Analyst
Transcript
- Lucas: A few years ago, a woman named Priya — former marketing coordinator, five-year gap after her second child — applied for a data analyst returnship at a mid-size healthcare company in Ohio. She didn't get it. Luna: Oof. That's a rough start to the story. But I'm guessing it's not where it ends? Lucas: Not at all. She didn't get it because she didn't have the technical skills yet. So she spent the next nine months — while still caring for her kids — earning the Google Data Analytics Certificate on Coursera, building a portfolio project using public hospital readmission data, and then she reapplied to the same company's next returnship cohort. This time she got in. Luna: That's a great example of treating a rejection as a gap analysis. She figured out exactly what was missing and closed it. Lucas: Exactly. And that's what we're going to dig into today — how a career gap can actually be an advantage when you're pivoting into a field like data analytics, because you have the time and focus to build skills from scratch. Priya's story is a template anyone can follow. Luna: So let's start with the certificate. The Google Data Analytics Certificate is pretty well-known now. What made it the right choice for her? Lucas: A few things. First, it's self-paced and costs about $39 a month on Coursera. She finished it in five months while her youngest was napping. Second, it covers the core tools — spreadsheets, SQL, Tableau, and R programming. You don't need a math degree. She spent a total of $195 and came out with a credential that's recognized by over 150 employers including Google themselves. Luna: And she didn't stop at the certificate. She built a portfolio project. That's huge for someone without professional data experience. Lucas: Right. The certificate alone tells an employer you've done coursework. A portfolio project tells them you can actually apply it. Priya went to data.gov and downloaded a dataset on hospital readmissions for Medicare patients. She cleaned it in Excel, analyzed it in SQL, built a dashboard in Tableau showing which hospitals had the highest readmission rates for heart failure, and wrote up a two-page summary of her findings. She hosted everything on a free GitHub Pages site. Luna: That's smart. She used public health data, which ties directly to the healthcare company she was targeting. So she wasn't just showing technical skills — she was showing domain knowledge. Lucas: Exactly. When she reapplied, she led with that project in her cover letter. She said, 'I didn't have the skills last time, so I built them. Here's proof.' The hiring manager later told her that project was a big reason she got the interview. Luna: Let's talk about the returnship itself. How did she find it, and what was the structure? Lucas: She found it through a site called iRelaunch — they maintain a database of returnship programs across industries. This particular one was a 16-week paid program, $25 an hour, 30 hours a week. The expectation was that you'd be re-skilled and ready for a full-time role by the end. Priya's cohort had eight people, four of whom had been out of the workforce for more than three years. Luna: And she got the full-time offer at the end. What was the salary jump? Lucas: The returnship was at $25 an hour, which is about $52,000 a year if you annualize it. But when they converted her to full-time as a data analyst, she negotiated to $72,000. That's a 38 percent increase. And she got a flexible schedule — three days in the office, two at home — which was critical for her as a parent. Luna: She negotiated? After a five-year gap? That's impressive. How did she approach it? Lucas: She did her homework. She used Glassdoor and the Bureau of Labor Statistics to find that the median salary for a data analyst in her area was around $68,000. She also knew the company had a strong conversion rate from the returnship — about 80 percent of participants got offers. So she had leverage. She said, 'I've proven I can do the work, and I'm bringing a perspective that's different from your typical new grad. I'd like $72,000.' And they said yes. Luna: I think that's a key lesson. Returnship participants often feel grateful just to be back, but Priya showed that you can still negotiate — especially if you've outperformed expectations during the program. Lucas: Exactly. And the other thing she did was ask for flexibility upfront. She didn't wait until after she accepted. She said, 'I can commit to 40 hours, but I need to be able to pick up my kids at 3:30 twice a week. Can we structure my hours so I start earlier on those days?' They agreed to 7:30 to 3:30 on Tuesdays and Thursdays. Luna: That's a good model for anyone re-entering with caregiving responsibilities. Ask for what you need, but frame it as a win-win. She was essentially offering to work earlier to cover the same hours. Lucas: Exactly. So Priya's story has four clear steps: identify the skill gap, get a credible credential, build a targeted portfolio project, and apply to a returnship program that matches your target industry. But I want to zoom out for a second — because this whole conversation is possible because we keep this show ad-free. No sponsors, no commercials. Just the information we think is useful. Luna: Yeah, that's intentional. We don't run ads because we want every episode to be something you can actually use, not something that's interrupted every seven minutes. If you find that valuable and want to support it, the link is buy me a coffee dot com slash fexingo. No pressure, just an option. Lucas: And it genuinely helps. Even a small contribution lets us keep doing deep dives like this one. So back to the data analytics path — there's something else I want to mention about the interview process for returnships. Luna: What's that? Lucas: Priya said the interview was less about technical trivia and more about problem-solving. They gave her a dataset during the interview — a CSV file with patient appointment data — and asked her to find patterns. No SQL, just Excel. She had 30 minutes. She used pivot tables to show that no-show rates were highest for Friday afternoon appointments. That was the kind of insight they wanted. Luna: So they were testing her ability to think like an analyst, not just memorize syntax. That's common in returnships — they assume you'll learn the tools on the job. Lucas: Exactly. And here's the thing — her gap actually helped her in the interview. When they asked about her time away, she said, 'I spent that time learning to be resourceful, to manage competing priorities, and to solve problems without a manual. Those are skills I'm bringing to this role.' She reframed the gap as a strength. Luna: That's brilliant. She didn't apologize for the gap. She made it part of her narrative. And the data shows that employers are becoming more open to that. LinkedIn's 2025 Workforce Report found that mentions of 'returnship' in job postings were up 40 percent year-over-year. Lucas: Yeah, and a lot of that growth is in fields like data analytics, where demand outstrips supply. Companies are willing to train people who have the right mindset. Priya's story is proof that you don't need a computer science degree — you need curiosity, discipline, and a willingness to start small. Luna: So for someone listening right now who's thinking, 'I've been out for three years, I don't know SQL, but I want to do this' — what's the first thing they should do today? Lucas: Go to the Google Data Analytics Certificate page on Coursera. You can audit the first week for free. Watch the first video on how data drives decisions. If you're still interested after that, sign up. And while you're there, start browsing returnships on iRelaunch or Path Forward. Don't wait until you're 'ready' — you'll be ready enough after the certificate. The portfolio project and the interview will teach you the rest. Luna: And don't be afraid to apply even if you feel underqualified. Priya's first rejection taught her what she needed to work on. That rejection was a gift in disguise. Lucas: Exactly. So here's my question for you, Luna — and for our listeners. If you could build a portfolio project today using free public data, what dataset would you pick? What question would you try to answer? Luna: That's the perfect way to end this. For me, I'd probably look at city bike-share data — see if there are patterns by weather or time of day. It's relatable and easy to visualize. Lucas: Great choice. And if you do build that project, let us know. We're at The Comeback Career Podcast. And if you found this useful, consider supporting the show at buy me a coffee dot com slash fexingo. Thanks for listening.