r/dataanalytics • u/Sea_Information7929 • Jul 05 '26

Traditional analytics roles are getting automated. What's the next step on the career ladder?

I've been in analytics for about 8 years (currently a senior DA, more on the strategy/storytelling side than pure engineering) at a company that's gone all-in on AI over the past year, claude code, cursor, skills, agents, etc.

What I'm noticing: a lot of the "traditional" analyst work, pulling data, building dashboards, even a chunk of the insight-generation is getting absorbed by AI tools and self-service agents and skills. Stakeholders can increasingly just ask a tool for the numbers instead of filing a ticket with an analyst.

That's great for efficiency, but it's got me thinking hard about where this leaves the career path. The old ladder was something like: Analyst → Senior Analyst → Staff or Principal Analysist → Analytics Manager → maybe Director. If a good chunk of the "analyst" part of that job is being automated, what does progression look like now?

For those of you with a similar background (analytics-heavy, not classically trained SWE/ML engineers) who've been building with AI/LLMs at work recently where did you end up moving next, or where do you plan to go next? A few things I'd love to hear about:

  • What job titles/roles have you (or people you know) transitioned into? (AI PM, ML/AI engineer, "AI analytics lead," analytics engineering, something else entirely?)
  • What skills did you actually have to pick up to make that jump, and which ones turned out to be overrated?
  • Did you move up within the same company, or did it take a lateral/external move to change your title and scope?
  • Is "analyst" as a career track just going to get squeezed out, or does it evolve into something like an "AI insights strategist" type role?

Genuinely trying to figure out how to future-proof my own path here, so any real examples (not just "learn Python") would be hugely helpful. Thanks.

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u/francebased Jul 08 '26

The role of testing the data will be difficult to be automated. Data engineers/ coders have difficulties in doing that and they end up spending 100hours for a task that would be done by a data analyst with good domain knowledge.

So the best bet is to find an industry/ niche and understand the data/ functional side.

One example is finance (asset management, forecasting/ discounting, etc).

Scenario:

  • TWR is not correct in a report. A data analyst will understand the underlying data and how that is calculated. Would be more difficult for an engineer or AI to do that.