Ian Klosowicz
Most companies don't expect you to be an expert when you show up. They know you're junior. The word is in your title.
What they do expect is that you won't need hand-holding on the basics — that you can open a dataset, write a query, and figure out what's going on without someone sitting next to you for the first three weeks. That's the actual bar.
I hear from a lot of people trying to break in, and the panic around "what if I don't know enough" is one of the most consistent things that comes up. So here's a straight answer: this is what a junior analyst is actually expected to know on day one, and this is what you can stop worrying about.
SQL is the closest thing to a hard requirement for a junior analyst role. If you walk in and can't write a query, most teams will struggle to use you in the first few weeks — and that's a bad start.
The good news is that "SQL" in a junior context means a pretty specific set of things. You're not expected to optimize query performance or write window functions from memory. You're expected to:
That's most of what a junior analyst does with SQL in the first 3 months. The more advanced stuff — subqueries, CTEs, window functions — comes up, but usually with enough runway that you can learn it on the job as you go.
I work in data engineering now, and I still use the same core SQL patterns every day. The functions that show up in 90% of real work are a much smaller list than most SQL courses lead you to believe.
Excel gets over-listed in job descriptions. When companies say "proficient in Excel," they usually mean you can do useful things with it, not that you've memorized all 400-plus formulas.
On day one, the expectation is:
If you can do all of that fluently, you're ahead of most candidates. Nobody is testing whether you know array formulas on week one.
This one doesn't get listed in job postings, but it might be the most practically useful thing a junior analyst can bring on day one.
Every company has their own data. Their own table names, their own naming conventions, their own quirks. No amount of coursework prepares you for the specific mess you'll find when you first log into their database. What companies need is someone who knows how to orient themselves — who can look at an unfamiliar schema and start asking the right questions.
That means:
The analysts who stand out early aren't the ones who know everything. They're the ones who can figure things out without getting stuck.
A junior analyst who can pull clean data but can't explain what it means is only half-useful.
Day one communication expectations aren't high — no one is putting you in front of a VP on your first week. But you should be able to write a clear Slack message explaining what you found, structure a simple summary with a header and a few bullets, and flag a result that looks off without needing to be prompted.
The bar is: can you tell me what the data says in plain English? And can you flag a problem instead of assuming it's fine?
This is a skill most courses skip because it's harder to teach in a video. If you've been building projects and writing up your findings — even in a portfolio project — you're already practicing it.
If you're building your skills and want a structured way to get job-ready, the Analyst Hive program walks you through the exact deliverables companies look for, including how to write up your work and communicate findings like someone who's done this before.
This list matters as much as the one above.
You're not expected to know:
If a recruiter is telling you that you need to know all of this to be competitive for a junior role, either the role is mislabeled or the recruiter is running a filter that doesn't reflect how those teams actually operate.
BI tools in particular — Tableau, Power BI, Looker, Metabase — are almost universally taught on the job. Companies have their own licenses, their own templates, their own data sources connected. Even if you've used Tableau in a project, the first week is going to involve learning how their version is set up.
The same goes for their analytics platform. Most companies use some combination of Google Analytics, Amplitude, Mixpanel, or a custom in-house tool. You don't need to know all of them. You need to know how to learn a new tool quickly and ask good questions when you're stuck.
Other things commonly taught in the first few weeks:
The program at Analyst Hive is built around hitting exactly this baseline — not learning every tool, but learning which parts of each tool actually matter for analysts. Month one is about building the assets that prove you're ready: SQL skills, Excel fundamentals, and 3 portfolio projects that demonstrate you can work with data, not just talk about it.
The goal isn't to make you an expert. It's to get you to the point where a team can hand you a dataset on day one and you can do something useful with it.
Do junior analysts need to know Python on day one?
Most junior analyst roles don't require Python. SQL and Excel cover the majority of entry-level work. Python comes up more in analyst roles that lean toward data science, and those usually say so in the job description. If Python is listed, treat it as required. If it's not listed, don't worry about it.
What SQL level do you need to get hired as a junior analyst?
You need to be solid on SELECT, WHERE, JOIN, GROUP BY, and aggregation functions. You should be able to write a multi-table query that answers a real business question. That's the threshold most technical screens are testing. Window functions and CTEs are useful but rarely a hard requirement for junior roles.
Will my employer teach me their BI tool?
Almost always yes. Companies know their stack is specific to them. What they're hiring for is someone who can learn tools quickly and already knows the underlying data concepts. Show up knowing SQL and how to interpret data, and the tool training follows.
Is it a problem if I've only used practice datasets, not real company data?
Every analyst starts with unfamiliar data. Companies aren't expecting you to have worked with their data before. What matters is that you can orient yourself in a new dataset — run basic checks, understand the structure, ask smart questions. That's a skill you can build on practice data if you approach it deliberately.
How long does it take to get to this baseline from scratch?
Realistically, 3 to 6 months of consistent work. I went from knowing nothing to getting hired in about a year, but I wasn't doing it full-time. With a structured daily plan, most people can hit the job-ready baseline in 90 days. The key is working on all of it — SQL, Excel, and real projects — at the same time, not sequentially.
If you want that structure, Analyst Hive is built around exactly that kind of daily progression.
