From Engineering Leadership to Building Something of Her Own

You’ve spent nearly two decades in engineering — leading teams, working in product, and scaling big data systems. That’s quite a journey. Where did it begin?

It started with curiosity more than anything else. I was always interested in how things actually work, not just the code but the systems around it and the people building them. I studied engineering because I liked solving problems, but I quickly realised the most interesting problems weren’t purely technical.

They were about people. How teams function. How ideas get translated into something executable. How you take a messy business need and turn it into something an engineering team can actually build. That intersection became the part I gravitated toward most.

Over time, I moved through different roles, leading full-stack teams, spending around seven years in product, and later moving into big data engineering leadership. Each step added a different lens.

Product taught me to stay grounded in the user and not get lost in what’s technically elegant. Engineering leadership taught me how hard it is to scale both systems and people without one breaking the other. Big data forced me to work with incomplete, noisy information, which interestingly is also what hiring often feels like.

You spent several years in product before returning to engineering leadership. That’s not a very typical path. How did that shape your thinking?

It changed things quite a bit. Product shifts your mindset. You stop thinking “can we build this?” and start asking “should we build this, for whom, and why now?” That framing stays with you.

Coming back into engineering leadership, I found I was much more comfortable bridging the gap between business and engineering. I didn’t see them as separate worlds anymore, and that ability to translate between them became a big part of how I worked.

In hindsight, that perspective is probably what eventually led me toward ReadySetJob, because I kept seeing the same hiring problems repeat themselves, and I couldn’t ignore the fact that the tools being used hadn’t really kept up with the reality of the market.

The Hiring Problem That Wouldn’t Go Away

What made you feel this was something worth building a company around?

It wasn’t one moment. It was more like a pattern that built up over time.

Being involved in hiring across engineering and product, I kept seeing the same frustrations on both sides. Candidates applying into what felt like a void, hiring managers overwhelmed with CVs that all looked the same, and in the middle of it strong people were simply getting missed.

At the same time, the broader context was shifting. The World Economic Forum’s Future of Jobs Report 2025 highlights that a large share of core skills will change by 2030. That makes traditional CVs even less reliable as a signal of real capability.

On top of that, AI is reshaping how work itself is structured. Research from organisations like Anthropic and BCG points in the same direction: roles are not just disappearing, they are evolving quickly. That creates a mismatch where companies are hiring for roles that are still changing, while candidates are trying to describe skills that are still forming.

Yet the hiring process hasn’t really adapted. CVs are still static, and screening is still heavily keyword-driven. At some point I stopped thinking “this should improve” and started thinking “this probably needs rethinking.”

From the employer side, what stood out most?

How much signal gets lost.

I’ve seen candidates filtered out in seconds because their CV didn’t contain the “right” wording, even when a conversation would have shown they were a strong match. That’s not a talent issue, it’s a process issue.

And on the company side, I’ve seen teams spend months hiring, speak to dozens of candidates, and still end up making poor decisions because the process relied too heavily on intuition rather than structured evaluation.

There’s also plenty of evidence that labour markets suffer from persistent matching inefficiencies, meaning the talent is there but the system doesn’t surface it well.

The human cost is what makes it hard to ignore. Job searching is already stressful. When good candidates are filtered out or ignored because systems can’t interpret their experience properly, it becomes demoralising. That part always stayed with me.

Building ReadySetJob, From Idea to Reality

How did you go from recognising the problem to actually building something?

Honestly, it wasn’t linear at all.

I had the technical background, product experience, and plenty of exposure to the problem, but building something from scratch is a completely different challenge, especially without the structure of an organisation behind you.

Every decision is yours, and every mistake is yours too.

What grounded me early on was speaking to users. A lot of conversations with job seekers, just listening. The same patterns kept showing up: long job searches with no feedback, experienced professionals feeling invisible, and graduates struggling to translate their experience into something employers would recognise.

Hiring managers had their own frustrations too, too much volume, too little signal, and processes that often felt more performative than effective.

That alignment made the problem feel very real on both sides.

From there, it became an iterative process: build, test, break, rebuild, repeat. My engineering background helps me move quickly, but my product experience helps me avoid building things that don’t actually matter.

Last question, what does success look like for ReadySetJob?

Success, to me, is very human.

It’s someone who was ready to give up finding an opportunity they’re genuinely excited about. It’s a company discovering a candidate they might have filtered out, who turns out to be one of their best hires.

I want ReadySetJob to close the gap between who someone really is and how they come across in a hiring process.

The job market is changing quickly, skills are shifting, AI is reshaping roles, and people are trying to keep up in real time. That creates uncertainty. If we can make that process a little clearer and fairer, that matters.

I’m not trying to build the biggest company. I’m trying to build something useful for people navigating real career uncertainty and for companies trying to hire better, not just faster.

That’s what keeps me going, even on the harder days.