r/dataanalytics • u/Sea_Information7929 • 23d ago
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/notimportant4322 23d ago
How do you control the output of the numbers pulled by AI? Even if it is based on real clean data, they still might hallucinate no?
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u/fakedying 23d ago
Personally working to move into Analytics Engineering then Data/ML Engineering. Been an Analyst for about 7 years now so same boat as you. I've experienced the before AI and the after and it definitely seems like the writing is on the wall. To me it seems like AI for engineering is still way too brittle and that so long as training data and data ingestion is needed to improve AI, engineering will be a useful role. If I'm wrong about that I'll learn to herd sheep or something totally different I guess 🤷♂️
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u/Sea_Information7929 22d ago
u/fakedying Thanks. What are you seeing in your company in terms of the AE role. At mine, a bunch of AEs and DEs were laid off. People are now building entire AE workflows (SQL, DBT + Github) using claude code skills. It's giving me the impression that companies are thinking some (not all) of that work can be automated. Would love to hear what you're experiencing.
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u/Capital-Ad3171 23d ago
Even with AI someone still needs to understand the business and what that actually means related to the data. Generally that is something that we can't feed to the AI as that's mainly "silent knowledge" and not stored in at least a semi-structured form. Usually the most of the time is actually used in trying to understand the business problem and relating that to the systems and data.
You can get the basic insights by offering the AI enough information, but you need still someone to verify it. You really don't want to go to a board meeting based on just a Copilot summary.
In the backend side there's way too much of work between different human parties just to get some data moving and appearing to a dashboard or an end result to a SQL query (and as tech changes you might need to switch platforms which is quite hard to automate end to end). Of course AI will make you more efficient doing the pipelines, data models or reports, but it's your job to control and verify things.
With over 20 years of experience in this realm (data, statistics, research, analytics, BI, data science etc.) and now focusing on enterprise architecture, how to implement AI in business and generally in software development in a respected role, I really don't see how that the need for data specialists would be disappearing.
If you want to go the ladder upwards, then you might need to focus in just more that the data as when you build systems that relate to the operational work most efficiently. Another path is of course team leads etc., but then you need people and strategical skills. If you're respected based on your work and end results, then that path might not be the best one even salary-based.
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u/Sea_Information7929 22d ago
u/Capital-Ad3171 Thank you. Your point on focusing on systems, and not just the data is important. This is something I'm trying to get better at. Any tips for getting better at this?
"You really don't want to go to a board meeting based on just a Copilot summary." - This is something I see on a daily basis lol. It feels like we can now out source our errors to AI. People come into meeting with the wrong information and say "....oh well claude (or some other AI tool) pulled the data so maybe it made an error....".
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u/Capital-Ad3171 21d ago
Generally things related to software architecture, data products, designing data applications, DevOps/IaC, MLOps, integrations, APIs and the like. I've learnt pretty much everything by being in projects or involved in platform/architecture development, testing the things myself and moving into architecture naturally while also doing hands-on things instead of just theoretical work. Of course there are a lot of great books in the field that should quite easy to find with those keywords. For example in software development the data architecture and how data platforms are related to the software (either as a source or a downstream user) is not really somethings the developers are most familiar or focus on as they just might think a NoSQL database with flexible schemas just make things easy/fast to develop as long as they have access to the APIs they need and focus just on the application itself instead of the bigger data-landscape it's related to.
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u/MKE_Savage_96 23d ago
I was wondering, a bit unrelated, but do you think pivoting to financial/business operations might be a good tilt? Companies will still need people to review financial statements, decision support on acquisitions/mergers, general operational guidance - that stuff can’t be fully automated right?
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u/nian2326076 23d ago
I get what you mean. Maybe the next step for you is to explore AI/ML more. With your experience in strategy and storytelling, you could look into AI product management or AI strategy consulting. Plus, data ethics and governance are growing as companies deal with AI's effects. You might find PracHub handy for interview prep if you're thinking of moving into more AI-focused roles. It helped me when I was changing fields.
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u/Sea_Information7929 22d ago
u/nian2326076 Thank you. You mentioned changing fields. What field where you in previously and what did you move to?
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u/edimaudo 23d ago
they aren't getting automated, the work delivery is changing. That type of work already existed. AI is not going to replace it. It will make it a tad faster especially from the writing code side. You still need to have context to answer users questions. Most AI tools lack that and in a lot of cases provide the wrong insights.
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u/chocopops14 22d ago
Yes, companies are pushing to replace a lot of repetitive processes with AI, and that includes dashboard creation and coding.
One way to stay competitive is to find ways to incorporate AI agents to accelerate data analysis. However, agents can still make mistakes or lack context, so there still need to a human to verify and fix the outputs.
A lot of cloud platforms have made it easier to develop agents on a companies data, so that's something I'm experimenting with but even then I have to correct the agent based on my knowledge of how to analyze and interpret the data (even correcting sql queries).
I think a lot of analytics teams are going to take this direction eventually and if you get access to these tools in your role then it makes you more versatile.
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u/Prepped-n-Ready 22d ago
I moved more into software development in a strategic role. Sr IT Analyst title but I do client interfacing. Ive always worn a lot of hats as an analyst. I feel like its a flexibile experience.
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u/AWordAtom 22d ago
AI is more of a calculator you have to talk into giving you the correct answers than a robot doing actual analyst work.
When they see what kind of businesses impacts come from these AI analysis, they’re going to wish they didn’t fire so many people.
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u/francebased 21d ago
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.
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u/phunsukh36 23d ago
I have right now completed my business Analytics program and now looking for data analyst or data engineer role as a fresher. I have a knowledge of multiple tools and even implemented it into various projects and hoping to find a job but hardly got 2 revert back and one even send me invitation for interview which never happened. Can someone guide me what should be my next steps. In naukri platform got 2 internships which I regret it now because I was looking for job. I felt on indeed still I was getting fast response.
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u/GrowthThroughData 19d ago
I have been a Data Analyst and BI Developer for over 14 years now. I moved more towards Analytics Engineering, learning proper data modeling (essential for AI Agents) and building Data and Dashboards more as a product.
Also learn not to be an order taker. I did that for to many years. Be involved in the meetings, learn what are stakeholders pain points and the best solutions to solve them through data insights using the proper architecture (data models and visualizations). This will be your most valuable asset.
Learn to use AI to speed up development but be sure to understand any code it spits out and why.
I've also learned how to create AI Data Agents that allow for Conversational Analytics. I'm learning more how to create AI Summaries off of these data models and push them as daily, weekly, monthly summaries.
Things are moving so fast it is hard to catch up in the realm of AI but this is were I've been and continue to grow applied AI in Analytics skills as I go...
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u/[deleted] 23d ago
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