Data Analyst

The most crowded doorway in tech

More people are trying to become a Data Analyst than almost any other role in technology, and most of them are following the same advice. Learn SQL, learn Python, do a bootcamp, build a portfolio, apply.

The advice is not wrong. It is just that several hundred other applicants for the same junior role have followed it, produced similar work, and written similar resumes. The competition is not the job. The competition is being distinguishable.

That shapes everything below. The goal is not to prove you can analyse data, because at this point that is assumed and cheap to demonstrate. The goal is to prove you can analyse a business.

The portfolio project problem

The standard portfolio is three notebooks: a public dataset, some cleaning, some charts, a model at the end. Titanic survival, house prices, a Netflix catalogue.

Hiring managers have seen these hundreds of times and they carry almost no signal, because the hard parts of the job were removed before the candidate started. The data was already clean, the question was already chosen, and no decision hung on the answer.

A project that does carry signal has three properties.

You chose the question. Something you actually wanted to know, ideally about a domain you know. Someone who worked in restaurants analysing delivery platform economics has an edge over someone analysing a dataset they downloaded, because they know which findings are surprising.

The data was messy or you gathered it. Pulling from an API, scraping something public, or joining two sources that were not designed to be joined. That is what the job is, and it is what the clean-dataset projects skip.

It ends in a recommendation, not a chart. What should someone do differently, and what would you need to be true for that to be wrong. Analysts who stop at description get hired as report builders. Analysts who make a call get hired as analysts.

One project like that beats five of the standard kind, and it gives you something to talk about for twenty minutes in an interview.

SQL is the gate, and it is tested live

Nearly every Data Analyst process includes a SQL assessment, and it is where most candidates are eliminated. Not because the questions are exotic, but because people learn SQL by reading rather than by writing under pressure.

What actually comes up: joins, and knowing what happens to row counts when a join is not what you assumed. Aggregation with group by and having. Window functions, particularly row_number, rank, and running totals. Date handling. Common table expressions to make a multi-step query readable.

The most common failure is not a missing function. It is a candidate who writes a query that runs, returns something plausible, and is quietly wrong because a join duplicated rows. Get in the habit of checking your row counts before and after every join, and say out loud that you are doing it. Interviewers notice.

Practise by typing, on a real database, against a clock. Reading solutions builds recognition, which is not the same skill as production.

Excel is not beneath you

Postings mention SQL and Python and candidates prepare for those. Then the take-home arrives as a spreadsheet, or the interviewer asks how you would explain a result to a finance team that lives in Excel.

A great deal of real analytics still happens in spreadsheets, and dismissing them is a tell that a candidate has learned the field from course material rather than from work. Pivot tables, lookups, and a clean model that someone else can follow are genuinely useful and worth naming on the resume.

The same applies to the business intelligence tools. Whether the company uses Tableau, Power BI, Looker or something else, the underlying skill transfers, but name the ones you have actually used. Dashboard tools are cheap to claim and easy to test.

What "analyst" means at different companies

The title covers a wide range and the postings do not always make it obvious.

At a large company with a data team, an analyst usually serves one function, marketing or finance or operations, and works from a modelled warehouse someone else maintains. The job is depth in a domain and clear communication with stakeholders.

At a smaller company, the analyst is often the entire data function. That means building pipelines, maintaining the warehouse, and owning the tooling as well as the analysis. Postings mentioning dbt, Airflow, or "own our data infrastructure" are describing an analytics engineer with an analyst's title.

At an agency or consultancy, the work is project-based across clients, with less depth in any one business and more range.

Read the posting for which one it is, because the interview will follow it. A candidate prepared for stakeholder work who gets asked about pipeline orchestration has read the wrong signals.

The take-home

Data Analyst take-homes usually hand you a dataset and a vague business question, deliberately.

The trap is to answer only the question asked. The candidates who stand out do three things: state their assumptions, since the question is under-specified on purpose; check the data quality and say what they found, since the dataset has problems planted in it; and finish with what they would do next given more time.

Keep the output short. A three-page memo that a manager could act on beats a thirty-cell notebook of exploratory charts. If you send a notebook, put the conclusion at the top and the working underneath. Analysts who bury the answer are judged as people who will bury the answer at work too.

Certificates, degrees, and what actually gates

Career changers spend a lot of money on this question, so it is worth being blunt about what the evidence in a hiring process actually looks like.

A certificate gets you past nothing on its own. Hiring managers see the same three or four programme names on hundreds of resumes and have learned they predict very little, because completion measures persistence rather than capability.

What a certificate does do is give you structure while you learn, and something to put in the education section so the resume does not look empty. Both are real. Neither is the reason you get called.

A degree matters at some employers and not at others. Large enterprises, banks, and government roles often filter on one. Startups and mid-size technology companies mostly do not, and will happily hire someone whose evidence is a portfolio and a good SQL screen.

What consistently gates is the SQL assessment and the take-home. Every candidate reaches those, and they are the same difficulty for a graduate and a career changer. Time spent practising queries under a clock is worth more than another course, and it is free.

If you are choosing where to spend the next three months, choose the messy project and the SQL practice over a second certificate.

Switching in from another field

Most Data Analysts came from somewhere else, and the domain knowledge you already have is your advantage, not the thing to hide.

Someone moving from nursing understands clinical operations data in a way a computer science graduate does not. Someone from retail knows what inventory data actually means. Someone from teaching has explained complicated things to people who did not want to hear them, which is most of the job.

Apply into your old industry first. The move from operations analyst in logistics to data analyst in logistics is much easier than the move into a sector you do not know, and the first role is what gets you the second.

On the resume, put the analytical parts of your old job at the top, in the target language. "Built the weekly reporting for a 40-person branch and rebuilt the model when the numbers stopped matching the finance close" is analyst work, whatever your title said.

Why the band for this title is so wide

We do not print salary figures, because a number from New York means nothing in Toronto, Bangalore or Nairobi, and a global average means nothing anywhere.

The reliable pattern is that this role has a wide band, and the position within it turns on how close your work sits to a decision that costs money. Analysts producing recurring reports sit lower than analysts whose findings change what a business does, at the same title and years.

The moves that reprice you: from reporting into decision support, from a support function into a revenue function, from a local company to a multinational in the same city, and picking up the engineering side, dbt and pipelines in particular, which shifts you toward analytics engineering and a different band.

For what the role pays where you live, our salary calculator takes your city and your years of experience.

This week

Pick one question about a business you already understand, get the messiest data you can find for it, and answer it in two pages ending with a recommendation. That single piece of work will do more for your applications than another certificate.

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