I had a conversation recently with someone I'd describe as exactly the kind of person who should be unfireable.

Fifteen years in financial data. Knew Bloomberg the way most people know their own kitchen. Could navigate FactSet's more obscure functions in under ten seconds. Had built half a dozen client relationships that renewed year after year, not because of pricing, but because he was genuinely embedded in how those clients operated.

He was calling me because he'd just been made redundant.

He wasn't surprised. That was the thing that struck me. He'd seen it coming. He just hadn't known what to call it, or where to go next.

I've been placing people in market data and financial technology for long enough to have watched the function evolve in real time. And something has shifted in the last eighteen months that most people in this space haven't fully processed yet.

It's not that financial data businesses are disappearing. Some of them are having a rough time, the market has been brutal to the ones built primarily on making public or licensed data searchable through a proprietary interface. But that's a separate conversation.

What I'm watching more closely is what's happening to the people those businesses built their commercial engines around.

The firms that thrived in market data over the last two decades needed a specific kind of professional. Someone who understood the data deeply, knew the workflow intimately, and could sit across from a portfolio manager or risk officer and translate a complex product into operational language. Interface expertise was a genuine career asset. Knowing your way around a terminal, understanding how a client's data architecture mapped onto your product, that was the moat.

Here's the problem. That moat is eroding.

When natural language becomes the interface, the value of knowing how to use the interface drops to near zero. The analyst who could run a screen in fifteen seconds because they'd mastered the function codes is no longer differentiated by that skill. The pre-sales engineer whose value came from deep product knowledge of a specific platform's query logic is working in a market where that platform is increasingly under competitive pressure from tools that require no query logic at all.

I'm not saying these people aren't good. The person I spoke to was genuinely excellent. But excellent at what is the question that matters now, and for many people, the honest answer is: excellent at something that's being abstracted away.

What I'm seeing in the roles that are actually being opened is a different profile entirely. The hiring leaders I speak to in this space, the ones scaling platforms, modernising data infrastructure, building AI-native products that compete in the same territory Bloomberg and FactSet have occupied, they're not looking for deep familiarity with legacy workflows. They're looking for people who can think about data problems from first principles. Who understand what makes a dataset genuinely proprietary versus commoditisable. Who can bridge the gap between a domain expert's methodology and the technical architecture that makes it executable at scale.

That's a different job. It requires some of the same raw intelligence. But it's built on different foundations.

The professionals I'm most concerned about are the ones sitting in the middle.

Not the people who built the technical infrastructure, they tend to be more adaptable because the underlying engineering problems are evolving rather than disappearing. Not the people at genuine strategy or leadership level, who are one step removed from the workflow disruption. But the large cohort of commercial, implementation, and product professionals whose market value was built on knowing a specific vendor ecosystem deeply.

These are people who are often very good at their jobs. They've been rewarded for that for years. And they're now being asked, implicitly, not directly, to reposition in a market that values a different thing.

Most of them haven't been told this clearly. And some of them are about to have a difficult year.

The positions I'm filling in this space increasingly sit at the intersection of domain expertise and system-level thinking. The ability to understand what a hedge fund actually needs from its data environment, then reason about how that should be solved, not just sold, is the skill that's in demand. Pre-sales is evolving into something closer to solution architecture. Customer success is evolving into something that looks more like embedded consulting. Implementation roles are being assessed for their ability to influence product development, not just execute delivery.

If the skill you've built is knowing how to use a tool, that tool is under pressure. If the skill you've built is understanding the problem the tool was solving, you have something more durable.

There's a version of this transition that goes well for people who are willing to do the uncomfortable work of reassessing their positioning. That means being honest, with yourself, and with the market, about what you actually know versus what you've been proximate to. It means identifying where your knowledge is genuinely irreplicable rather than simply familiar.

The person I spoke to at the start of this had more transferable insight than he realised. He understood how investment workflows broke down at the data layer. He'd seen it fail a dozen different ways across a dozen different clients. That's not interface knowledge. That's something more substantive.

He just hadn't been required to articulate it that way before. Because for a long time, the interface was enough.

It isn't anymore. The game's changing. Are you ready?

Richard