Hey. I am a final year student majoring in AI & Data Science aspiring to get into analytics. It’d be great if you could share what companies generally look for while hiring interns/freshers for full time analyst roles. Thanks!
"Or similar" is doing a lot of work in that sentence.
When an employer lists BI tools in job posting as interchangeable, they're telling you - in writing - that the interface is not what the salary is for. The salary is for SQL, modeling, metric ownership, whatever the rest of the posting says. The tool is a delivery mechanism, like they don't care which brand of laptop you bring.
Which is uncomfortable if your entire professional identity is "Power BI person" or whatever BI person. Your subreddit karma, your certs, your muscle memory for the formatting pane - all real, all earned, all attached to one vendor's interface roadmap.
To be clear: deep Power BI knowledge still pays, in platform and enterprise roles it pays well. But those are specific roles, not the default analyst path. For the default path, "or similar" is the market saying the quiet part.
Quick self-audit: if your company switched to Looker tomorrow, what percentage of your value survives the migration? SQL survives. Modeling survives. Knowing why finance and sales disagree about revenue survives. The rest is interface.
I'd want my number above 70. Mine wasn't always.
Under the title Power BI Analyst there are two completely different professions, and nobody tells you to choose.
Career one: business and product analytics. SQL, metrics, experimentation, cohorts, stakeholder wrangling, domain knowledge. Power BI is your delivery van. Nice van. Still a van.
Career two: BI platform and engineering. Semantic models, Fabric, deployment pipelines, performance, security, governance, CI/CD. Power BI is the platform itself, and you're the one keeping it trustworthy at scale.
Both are real. Both pay. They grow in different directions, get promoted for different things, and interview completely differently.
The trap is the default path: learning a little of both. Some DAX patterns, some Fabric, a bit of experimentation theory, half of governance. Feels like breadth. Reads as junior in both interviews - the platform people see you don't know deployment, the product people see you've never owned a metric.
I'm not saying the split is absolute, seniors eventually reach across it. But you reach across FROM somewhere. Depth first, then breadth. The reverse doesn't work, it just feels like it's working.
So before the next course: which of the two are you actually building? If you can't answer, that's the thing to figure out this month. Not time intelligence.
The product post. I said it was coming.
Everything in this series comes down to one gap: chat AI answers your description of the problem. In Power BI the problem lives in the model - relationships, grain, filter direction, dependencies. If the tool can't see the model, it's guessing politely.
So Brighter opens the PBIX. You bring a real broken file, it looks at the actual model, and walks the diagnostic path with you.
The annoying design choice, on purpose: it doesn't just hand you the fix. It shows how it narrowed things down and asks you to verify. When it sees you tripping on the same thing over and over - filter context, grain, whatever - it gives you practice on that thing and remembers between cases.
Because honestly: a tool that just fixes your file makes you faster this week and more replaceable next year. The learning loop from junior work is dying. If AI killed it, AI tools should be rebuilding it. Not selling you the speed of losing it.
That's the bet. Ask me anything, including "why not just ChatGPT" - the previous post is the answer, but ask anyway.
Or just try Brighter