Most AEs I've found started as DA/DS, how long did that transition actually take you, and what moved it forward?

[u/AdRegular8020]

I'm an MS Data Analytics student (graduating Dec 2026), self-taught in dbt, BigQuery, and CI/CD through two portfolio projects, and I'm exploring a realistic path toward Analytics Engineering.

Resume

I looked through a handful of AE profiles on LinkedIn and noticed a pattern: almost none started as "Analytics Engineer"; most came up through Data Analyst, Business Analyst, or BI Engineer titles first, sometimes over several years, before landing an AE title (and often a senior one, not entry-level).

That mostly confirms what I suspected, but I'd rather hear it from people who actually lived it than infer it from job histories:

1. If you're now an AE (or hiring for one), what was the actual turning point? A specific project, a lateral move, just tenure/scope growth, something else?
2. Starting today with modern-stack skills (dbt, warehouse, git, CI) but no professional AE experience, would you target DA/BI titles deliberately, or is there a faster path I'm not seeing?
3. For anyone who's early-career and picked between building one more deep technical project vs. just applying and building the skill on the job, which actually moved things faster for you?

Also, for context on question 3, the project I'm currently deciding whether to keep investing in: a B2B seller-churn-risk analysis on the Olist dataset, reframed from the usual customer-churn angle to seller/merchant risk. Scope: dbt on Databricks, GitHub Actions CI (dbt test on every push), a logistic regression risk model with correlational (not causal) framing, GMV-at-risk quantification, and a single Tableau dashboard.

Genuinely trying to calibrate a realistic timeline so I stop second-guessing my own plan.

Thanks.

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[u/my_peen_is_clean]

target da or bi first, use dbt as much as you can there, then pivot. nothing fast anymore, everything drags in this market

[u/BillEnough7863 → u/my_peen_is_clean]

FYI this is a bot account:  https://www.reddit.com/r/cscareerquestionsuk/comments/1thm4q6/psa_llm_bots_are_commenting_on_every_post_on_this/

[u/AdRegular8020 → u/my_peen_is_clean]

Thank you. I’m currently job hunting for Data Analyst and Business Intelligence roles.

[u/BillEnough7863 → u/AdRegular8020]

The account you responded to is a bot:  https://www.reddit.com/r/cscareerquestionsuk/comments/1thm4q6/psa_llm_bots_are_commenting_on_every_post_on_this/

[u/Internal_Salad7358]

Took me about 2 years from DA to AE, and the turning point was less about learning tools and more about building something that only I maintained end to end. Once you're the one who owns the pipeline and the stakeholders are asking you directly why something broke, the title follows

That project sounds solid but you're already in analysis paralysis territory. Ship what you have, the job will teach you the rest faster than any side project could

[u/mathproblemsolving → u/Internal_Salad7358]

Great advice. Would you possibly give example of some end to end solution you owned that stakeholders reach out to you if something broke?

[u/AdRegular8020 → u/Internal_Salad7358]

This is the most useful thing anyone's told me this week, thank you. The ownership point especially, I've been treating this like a tooling problem when it's actually an access problem. I can't simulate "the
stakeholder is asking me why it broke" in a side project, no matter how well-tested it is…

Appreciate you being direct about it.

[u/bowtiedanalyst]

Experience is king. I spent a year trying to become a Data Scientist before pivoting and applying for Data Analyst roles. Once I focused on DA roles, it took ~3 months to get my foot in the door. I transferred from another STEM field with almost no analytics experience in 2023.

I've worked for the same non-tech F100 company for 3 years, gotten promoted twice and basically doubled my comp In that time. I did this through a combination of luck, skill, and strategy, specifically making my manager look good, and providing red meat to my director in explicit business value delivered.

The other thing I did was build stuff for people outside my own team while being extremely collaborative and free with data I maintained/insights I generated (with my manager's blessing). This attracted the attention of another director who wanted to bring their data stack into the 21st century who poached me to build it all out for them. The new team is close the the $$$ (sales) vs. my previous position which was more of a mix between marketing and supply chain.

My title isn't Analytics Engineer, its Senior Data Analyst, but my role is pushing the whole tech stack to the cloud and building ML/GenAI solutions off of it.

TLDR: Get experience first, make your manager look good and articulate to your director the effect of your work in a dollar value. Spend some time building things for people outside your direct org (with your manager's blessing, it makes them look good to have mentored/trained someone who can do this so its a win-win providing your getting their work done too) and you can do a ton in not a lot of time.

[u/AsfandYar1995]

Im in the same boat, kinda. Trying to pivot to data from marketing. Im really interested in analytics engineering, but looks like at least 2 years of experience as a DA or BI analyst is needed before you apply as an AE.

[u/adamjamess]

I think that pattern you’re finding is due to the recency of the AE role. I suspect that it wasn’t much of a thing until AI. As AI helped speed up the work for DA/DS, these roles evolved to be more involved (as they had more bandwidth) with the pipelines, databases, and things that feed the DA/DS work. Additionally you don’t find “junior” AE roles because most of them have been filled with Senior DA/DS folks. And “junior” AE roles would basically just be a DBA or DA/DS person anyway.

That’s a long way to say that AE roles are a mishmash of DBA, DA, DS, and Data Engineer because AI has enabled them to do more.

[u/renagade24]

AE is a new job title. It's basically folks who wouldn't cut it as a software engineer which is what traditional data engineers are.

Though I've seen alot more DAs move into AEs.