6 Data Jobs Born from the AI Boom

6 Data Jobs Born from the AI Boom

AI is not replacing data professionals. It is creating new ones.

Every week someone asks me on YouTube whether it is still worth learning data analytics. My answer is always yes — but the roles you are preparing for look different from the ones that existed when I started.

Back in my days 😅 data science wasn’t a role, and I even remember when University of New South Wales in Sydney introduced DS as a degree! Before that, all data scientists I knew were statisticians, actuaries or mathematicians in the past.

The field keeps evolving, so here are six roles of the (nearest) future worth knowing about.

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1. Analytics Engineer

This is probably the fastest-growing data role right now, and one of the least understood by people outside the industry.

An analytics engineer sits between the data engineer (who builds the pipelines) and the analyst (who answers the business questions). Their job is to take raw data and turn it into clean, reliable, well-documented models that analysts can actually use.

The core tool is dbt (data build tool), combined with SQL and a modern cloud data warehouse like Snowflake or BigQuery. If you already know SQL well, this role is more accessible than it looks.

Why it matters: As data stacks get more complex, companies need someone who thinks like an engineer but speaks the language of the business. That gap is exactly what analytics engineers fill.

Who it suits: Analysts who enjoy the structural side of data work — building things that other people rely on, not just answering one-off questions.

2. AI Product Manager (Data & Analytics)

Product management has always required a mix of business sense and technical fluency. Now it requires a third thing: understanding how AI and data models actually behave.

By the way, all Product Managers I know are usually incredibly well paid.

An AI Product Manager owns the roadmap for a product built on top of data or machine learning — a recommendation engine, a forecasting tool, an internal analytics platform. They do not build the models themselves, but they need to understand what the models can and cannot do, and translate that into decisions about what to build next.

Why it matters: Most organisations are building AI-powered products without anyone who genuinely bridges the technical and business sides. That person is increasingly valuable and increasingly scarce.

Who it suits: Analysts with strong stakeholder skills who want to move into a more strategic, product-focused role. Your data background is a genuine differentiator here — most PMs come from a business or design background and have to learn the data side. You already have it.

3. Forward Deployed Engineer

This one is less well known but worth understanding.

A forward deployed engineer (FDE) is essentially a technical problem-solver who works directly with clients or business teams to implement and customise software — often AI tools — in real-world environments. The role sits at the intersection of engineering, consulting, and customer success.

I know a lady who was a data scientist and recently transitioned into FDE role. Also my friend is running an interview preparation platform that also provides coaching services, he said FDEs are in such huge demand, that they can’t find enough of coaches, and the price is $500 per session.

Companies like Palantir made this role famous, but it has spread across the AI software industry. The job involves understanding a client's specific data problems, configuring or adapting a tool to solve them, and often writing code to make it work in their environment.

Why it matters: As AI tools become more powerful, the gap between "this tool exists" and "this tool works for our specific business" is growing. FDEs close that gap.

Who it suits: Analytically minded people who enjoy client-facing work and problem-solving under real constraints. Strong SQL and Python skills plus business communication are the core requirements.

4. Decision Intelligence Analyst

This is a newer job title, but the underlying idea is not new — it is what the best analysts have always done, now formalised into its own role.

A decision intelligence analyst does not just report on what happened. They use data to actively improve how decisions get made inside an organisation — mapping out decision processes, identifying where data should influence them, and measuring whether decisions are actually getting better over time.

Think of it as the difference between a dashboard that shows you revenue by region and an analyst who tells you which regions to prioritise investment in next quarter, and why.

Why it matters: Organisations are drowning in data but still making bad decisions. The demand for people who can close that gap — who can turn data into actual choices, not just charts — is growing.

Who it suits: Senior analysts and those with strong business acumen who want their work to have a more direct impact on strategy.

5. Analytics Translator

In many organisations, there is a persistent gap between what the data team produces and what the business actually uses. The analytics translator exists to close it.

For some reason, this job title sounds super appealing to me!

This role sits between technical teams and business stakeholders. The translator speaks both languages — they can sit in a meeting with a data scientist and understand what is being built, then walk into a board presentation and explain what it means and why it matters.

It sounds soft, but it is genuinely difficult. Most technically strong people do not communicate well. Most business-strong people do not understand the data. The ones who can do both are rare and well-paid.

Why it matters: AI is generating more outputs than ever — models, forecasts, recommendations. But those outputs are useless if no one in the business understands them or trusts them. The translator is the person who makes AI actually land.

Who it suits: Analysts who find themselves naturally spending more time explaining findings than producing them. If you are the person everyone asks to "just translate what the data team said," this might be your role.

6. AI Auditor / Model Validator

This one is being driven by regulation more than anything else.

As AI systems make more consequential decisions — credit approvals, hiring, medical diagnoses — governments and regulators are starting to require that those systems be independently checked. An AI auditor examines models for accuracy, bias, fairness, and compliance with relevant regulations.

It is a role that requires both technical understanding (enough to interrogate a model) and domain knowledge (enough to understand what the stakes are in that industry). Finance, healthcare, and insurance are the sectors hiring for this most actively right now.

Funny enough I just read an article about UK app ‘Just Eat’ using AI generated images for food menu (https://www.bbc.com/news/articles/crz981p0klvgo)

Tiger Prawn is by far my favourite:

So yes, AI auditor is definitely required.

Why it matters: This is one of the few AI-adjacent roles being actively shaped by legislation, which means demand is not speculative — it is being mandated.

Who it suits: Analytically rigorous people with an interest in ethics, compliance, or risk. A background in finance or insurance is a head start.

What these roles have in common

You can notice that none of them are purely technical. All of them need business knowledge, communication, and the ability to work across functions. And this excites me because it doesn’t put you in one box and opens up various avenues to grow further.

If you are building your data career right now, that is the direction to develop in. The tools are learnable. The judgment, the domain fluency, the ability to translate between worlds — those take longer to build and are harder to replicate.

The good news is that you are building them every time you work on a real business problem, explain a finding to a non-technical stakeholder, or push yourself to understand the industry you are working in.

Keep pushing 💪,

Karina

This week on YouTube

Let’s discuss recent layoffs and what to do to be ready - https://youtu.be/3aEdY8QsJjc

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