Data Analyst
You turn messy company data into the answer to a specific business question, and you make the answer believable.
01
What the job actually involves
- Writing SQL against the company's data to answer questions like why a metric moved, or which customers stop paying and when.
- Building dashboards people actually use, which mostly means removing things from them.
- Presenting a finding to people who will act on it, and being clear about how confident you are.
02
What you would need to study
- Any quantitative degree — Statistics, Economics, Engineering, Mathematics, B.Com with strong numbers
- Bootcamps and self-taught routes are genuinely accepted here if the portfolio is real
- This is one of the most credential-flexible careers on this list
03
Skills that matter
And which part of your Growth Profile each one builds, so they can be scheduled rather than just listed.
SQL — non-negotiable and testable
Skills
Excel or Google Sheets to a high standard
Skills
One visualisation tool: Power BI, Tableau or Looker
Skills
Statistical literacy — enough to avoid confident wrong answers
Academic
Writing a finding in three sentences
Skills
04
What the work is really like
- Desk work with reasonable hours in most companies — this is one of the better work-life balances in tech-adjacent roles.
- You sit between engineering and the business and are frequently the translator between them.
- Remote and hybrid work are common and widely available.
05
The hard parts
Every career page here carries this section. A page that only sells is no use to you.
- A large share of the job is data cleaning and answering ad-hoc requests that go nowhere.
- You are often asked to prove a conclusion someone already reached. Learning to handle that well is a career skill in itself.
- The market has a lot of entry-level supply. A portfolio of real analysis is what separates candidates.
06
Where it is heading
- Broad and growing demand across every sector, not just technology companies.
- Natural progression into data science, analytics engineering, or product and business roles.
- AI tools raise the floor on routine querying, which pushes the value toward framing the right question.
07
What to learn first
- SQL, taught through real query problems rather than syntax lectures
- Power BI or Tableau end to end
- Statistics for decision-making — sampling, significance, and what they do not tell you
- Python with pandas, once SQL is solid
08
Projects that prove you can do it
These build the Experience dimension — the part of a profile you cannot revise for the night before.
- Take a public dataset, ask a genuine question, and publish the analysis with the reasoning visible.
- Build a dashboard for a college society or a small business and get them to actually use it.
- Find a published statistic in the news and check whether the data supports the headline.
09
People worth talking to
Roles to seek out, not names. One honest conversation beats ten articles.
- A data analyst inside a non-tech company — retail, manufacturing, banking
- A hiring manager about what they look for in a portfolio
- Someone who moved from analyst into data science, about what the gap really was
Knowing the job is half of it. Knowing whether it suits you is the other half.
The assessment ranks all ten directions against how you actually like to work, then builds the plan to get you there.
Free to start · Nothing shared without your say-so