If you work in data right now, or you are trying to break in, this one is for you.
I get some version of this message almost every week:
"Is data analytics even worth getting into anymore? It feels like AI is just going to take over the whole thing."
You are not alone in thinking this. And honestly, it makes sense.
I have been in data for over 12 years, including my time at Google. I have watched tools reshape entire job categories before. What’s happening right now feels different. The tools are changing. The structure of the role is changing too. And most people in the field have not fully adjusted to what it is becoming.
Here is what I am already seeing happen to the Data Analyst role, and where it is going by 2030.
1. Entry Level Bar is Evolving
In 2025, every experience category for data analyst hiring grew except one: 0 to 2 years of experience. Entry-level demand declined while demand for mid and senior analysts continued to climb. At the same time, 66% of job seekers in data spent 3 or more months searching.
These data points may make it sound like entry-level jobs are disappearing, but actually that is not true. Your role as an entry-level data analyst is evolving. You're expected to know more than an entry-level data analyst back in 2020. This includes dashboards, coding and even communication. AI is now integrated into most tools an average Data Analyst is using. For example:
Snowflake Cortex Analyst lets business users type a question in plain English and get an answer back with the SQL already written and run. According to Snowflake's own benchmarks, it reaches around 90% accuracy when grounded in a proper semantic layer. Bayer is already using it in production.
Microsoft Copilot in Power BI is now enabled by default. Business users ask questions and Copilot builds the report, writes the DAX, and summarizes the insight. Microsoft's own documentation says it is designed for users with no technical background who would otherwise burden their data teams.
Why it matters: It matters because understanding this evolution in the data analysis tools is important so you can adjust your learning roadmap accordingly. Although this is not fully adopted yet, in the next five years we will see more and more companies using AI-integrated tools. Yes, you will still need a human to leverage these AI analytics tools.
Airtable for Interview Prep Template
Airtable 🔗 is an AI workflow platform that lets you build automated systems on top of your data. This is one of those tools I wish I had years ago. Instead of spending hours Googling companies, I built an interview prep app where I just drop in a company name and field agents do the rest: analyzing financials, researching product launches, scoping out competitors, pulling together interview tips. It's like having a research analyst on call. It auto-updates before each interview and I can add new companies anytime. You can access the Airtable interview prep template here. Thanks to Airtable for sponsoring this newsletter.
2. Data Analysts adopting Data Scientists' Work
This is the one I feel most strongly about. And it is the shift I am watching happen in real time.
For example, hypothesis testing, experiment design, sample size calculations, this work has always lived inside data science. Analysts were not expected to own it and were paid accordingly. That is changing.
Why it matters: As AI absorbs the repetitive work, data scientists are moving further up the stack into research and infrastructure. The basic experimentation and statistical work they are leaving behind is moving toward analysts. If you are mid-level right now, experiment design and core stats is the single most important area to go deep on. It connects your work directly to business decisions and moves you into compensation ranges that used to belong to a different title entirely. And if you an an entry level, you know what to learn next.
3. Fragmentation of Data Analyst Job Title
Right now data analyst means something different at every company. The job postings are already showing you where this is headed.
The titles replacing the generic role are already appearing in job postings today. Analytics engineer. Experimentation analyst. AI analyst. Decision scientist. Growth analyst. These titles are niche today. They will be standard by 2030.
Why it matters: Being a generalist will not be enough. essentially, at some point everybody would need to make a specialization because we will have more fragmentation and specialized work for data analysts. For example, AI analyst is a very new space, and there is a huge demand right now. If you consider yourself a journalist, you should seriously look into specializing.
A Final Thought
The data analyst role, like many others, is evolving. I don’t believe AI will replace analysts, data scientists, or data engineers, but it will absolutely reshape what the role looks like over the next few years. One of the best things you can do right now is anticipate that shift and start positioning yourself ahead of it. I’m curious, which of these predictions are you already seeing in your own role or job search? Reply and let me know. I read every response.
You can full breakdown of Data Analyst career predictions:
✨ What’s New with me?
There’s a lot happening right now. I just left my job at Google (see here). I’ll be going deeper into it in an upcoming newsletter. The next few months are packed with travel, I’ll be in DC speaking at the AI + Expo, then Vancouver for Web Summit, Vegas for Dell, followed by VivaTech in Paris and Cannes Lions in June. I also co-hosted my second in-person event in Seattle with my cohost Anjali and it was such a big hit, 70+ women in tech and AI showed up. We are already working on the next one in May. In between all of that, I’m really looking forward to slowing down a bit and spending the summer in Seattle with family.