The rise of the finance engineer

Before 2010, “revenue operations” was a niche career path.

In 2023, it was the fastest growing job in America.

The role didn’t spring up out of nowhere, though. Go-to-market teams were spending more time mining insights from their CRM, campaigns, pipeline, and customer data. Eventually, that work became valuable and complex enough to warrant its own job title.

Something similar is happening in the finance department.

Finance teams have more data than they know what to do with: actuals, forecasts, headcount plans, CRM inputs, billing data, board metrics, department budgets, and investor reporting. At the same time, AI is changing what it means to build, automate, and maintain the workflows that connect it all together.

Some of them are focusing less on report-pulling and more on designing the systems behind it: turning messy, recurring processes into systems that can run autonomously without losing the context, judgment, or controls finance teams are responsible for.

They're called finance engineers. And they're starting to get a lot more attention:

Part finance expert, part systems designer, part AI workflow builder, these roles are still taking shape. But they’re a good indication of what the next era of finance work will look like.

What is a finance engineer?

Ask five people, and you’ll get five different answers.

Numeric’s Parker Gilbert has a snappy definition: “Finance engineers treat their own operations as a codebase and every pain point as a potential solution to build.”

That’s a good starting point, and it’s reminiscent of another hybrid role that’s emerged recently: the “GTM engineer.”

Clay helped put a label around the GTM engineer in 2023: a hybrid operator who uses data, AI, and automation to build revenue workflows. A few years later, the title is showing up in high-growth companies around the world.

There’s good reason to think finance engineering will follow a similar path. Finance teams are in a similar environment: more data, more tools, more automation opportunities, and more need for someone who can turn messy workflows into scalable systems.

The role is still early enough that the definition depends on who you ask. But the direction is clear: finance teams need people who understand the function deeply enough to redesign how the work gets done.

Here are three paths we could see the role evolving into:

1. A software engineer with a finance role

The most literal version, but not the most common one.

A technical person joins the finance team to build data pipelines, automate workflows, maintain internal tools, and act as a dedicated technical resource. They don’t own the forecast or the close, but they understand enough of the process to support it and build around it. This person may have made a lateral move from data, analytics, product, or software engineering into finance to give the function more technical depth.

They can be extremely valuable, but they’re hard to find—engineers with strong finance chops are few and far between.

2. An automation-obsessed accountant

An accountant or controller starts deploying AI anywhere and everywhere: AI agents, MCPs, APIs, and automation tools. Instead of booking the same journal entry, chasing the same reconciliation, or managing the same close checklist every month, they recognize that well-designed AI workflows can handle most or all of these tasks.

With the time they get back, they’re able to focus on unique edge cases, higher-level strategy, and finding other places where AI and automation can spread throughout the function.

Essentially, they’re turning themselves into workflow orchestrators rather than number-crunchers: defining the logic, setting the controls, checking the output, and deciding where human review still belongs.

3. An AI and data-minded FP&A professional

This is the version that’s most relevant for finance teams.

It’s the FP&A or strategic finance person who understands the business logic behind the forecast, board deck, variance analysis, department budget, or revenue model, and uses AI and automation to make that work repeatable. They understand end-to-end finance, processes from journal entries to 5-year forecasts and everything in between. They’ve gotten their hands dirty vibe-coding one-off tools, and know how to leverage MCPs and AI agents.

A traditional FP&A analyst might answer the question: “Why did sales and marketing spend come in above budget?”

A finance engineer asks: “Why do we have to rebuild this analysis manually every month, and what system would make the answer show up automatically with enough context for the business to trust it?”

It’s not just a finance person that uses AI—that’s almost everyone at this point. It’s someone who knows how to redesign the work around AI: what data comes in, what logic gets applied, where agents are coming and going, and where human review and judgment reigns supreme.

Skills that compound in an AI-powered world

If all of this sounds futuristic, it is…somewhat. There are certainly finance pros spending their days doing the above right now, but like RevOps people in 2010, they’re exceedingly rare.

But given the pace of AI progress, these kinds of skills are likely going to be in-demand sooner than later. AI is already really good at a lot of the work that used to consume analysts’ time. Claude for Excel can build models, clean up formulas, summarize data, and generate first-pass analysis at the speed of a prompt. Being the person who can slice and dice a model using 100+ shortcuts is quickly becoming less of a moat.

So, the real in-demand skill becomes AI orchestration—deploying and managing AI agents, pressure-testing their work, and turning the output into a clear story for leadership and business partners.

That boils down to:

1. Finance fundamentals

Tomorrow’s finance pros may not be manually manipulating spreadsheets, but finance knowledge will still be invaluable. Agents will make mistakes. They’ll pull the wrong field, miss an edge case, overstate a variance, or produce an explanation that sounds right but doesn’t match how the business actually works.

Having a gut feel of when a number feels wrong, and the instinct to go check it, will be necessary to keep agents honest. That requires the same foundation that has always mattered in finance: understanding the P&L, knowing how forecasts are built, spotting weird movements, understanding timing differences, and knowing which assumptions actually drive the business.

2. AI fluency

Being comfortable working with AI is already one of the most important finance skills. And it’s only going to matter more going forward.

AI-fluent finance pros:

• Know how to prompt clearly and give AI relevant context
• Understand how to work with MCPs, AI agents, and spreadsheet-native tools like Claude for Excel
• Know when a workflow can tolerate some ambiguity (like summarizing a meeting), and when the output needs to be deterministic, auditable, and tied to trusted source data
• Have a willingness to vibe code one-off tools that can be used themselves or throughout the finance function

3. End-to-end workflow design

Instead of executing workflows themselves, finance pros will need to be able to design, review, and maintain automated systems.

This is a big mindset shift, as traditional finance work is task-oriented: close the books, pull the report, add the variance commentary, lock in the budget.

AI can handle a lot of those individual tasks on its own. But it still can’t reliably own an entire end-to-end workflow without guidance. So, the job of the finance engineer becomes figuring out which steps AI can handle and when they need to step in and handle exceptions.

4. Storytelling, collaboration, and people skills

An easy way to think about which skills will be valuable in the future is asking: what can’t AI do now or in the future?

Human-to-human interaction is near the top of the list.

AI’s writing and voice capabilities continue to improve. But it can’t read the room during a budget meeting, or recognize the need to carefully frame a glaring budget variance because it’s politically touchy.

More than anything else, it can’t develop relationships across the business.

Take the annual budgeting process, for example. There’s a certain amount of tension throughout—department heads ask for the world, then have to accept a final budget number that’s less than what they wanted. Good finance teams add a human buffer to this inherently tricky process, helping budget owners navigate the give-and-take and framing tradeoffs in a way that gets their buy-in.

The same goes for leadership interactions. A CEO or CFO may use AI to pressure-test a scenario, but they’ll still want a finance partner they can call up to ask follow-ups and talk through the implications.

This is where the best finance pros will separate themselves. They’ll use AI to handle the report-pulling and data-wrangling, then use the time they get back for work AI can’t replicate: building trust, influencing decisions, pushing back with context, and telling the story in a way that lands with the intended audience.

Start building tomorrow’s finance skills today

Job anxiety is everywhere right now, including finance. Scroll through r/FPandA and you’ll get a sense of the current mood among FP&A pros.

Nobody has a crystal ball, but the rise of the finance engineer is a pretty clear hint at where the value is moving. Less task-based work, more AI orchestration and human judgment.

Vibe coding is one of the most practical places to start. It teaches you to think less like someone completing a one-off task and more like someone designing a workflow.

We put together a guide to get you started: