Pick your data lane.
Each pathway targets a real junior role. Below: what each one is, what hiring managers look for, the one mistake to avoid, and the first project that proves you're ready.
Data Analyst
Build the skills companies actually hire for first.
Most junior analyst roles want the same core stack: SQL, Excel, and one BI tool (Power BI or Tableau). Python comes later. Before you add anything new, focus on cleaning data, answering a real business question, and explaining your work clearly.
Data Engineer
Learn to move data reliably before you learn to move it fast.
Junior data engineering roles are built around SQL, Python, cloud fundamentals, and ETL/ELT pipelines. Employers want to see that you can build something that works, stays reliable, and is easy for others to maintain. Tools like dbt and Airflow matter, but only once you understand why data flows the way it does.
Data Scientist
Frame the problem well. Build a baseline. Explain the tradeoffs.
Junior data science roles expect Python, SQL, statistics, and scikit-learn — but the real differentiator is judgment. Can you frame a business problem as a data problem? Can you choose the right model and explain why? Can you communicate results to someone who doesn't know what F1 means?
The full breakdown
Open a pathway to see what hiring managers look for, the common mistake, the proof you need, and your recommended first project.
- →Junior Data Analyst
- →Graduate Data Analyst
- →Reporting Analyst
- →BI Analyst
- →Business Data Analyst
- ✓SQL for querying and cleaning data
- ✓Excel for validation and ad hoc work
- ✓Power BI or Tableau for dashboards
- ✓Clear, non-technical communication
- ✓End-to-end project evidence — not just tool familiarity
Fix: Pick one project. Take it end-to-end: business question, clean data, dashboard, written insight. Then move on.
- •SQL on your CV with 2–3 concrete examples (not just "SQL")
- •1–2 dashboards in Power BI, Tableau or Looker Studio
- •A written 1-page insight summary per dashboard
- •A LinkedIn post or short Loom walking through one project
What to build: Take a public sales dataset (Kaggle or a Shopify sample). Load it into Postgres or BigQuery, write the SQL, and build a dashboard answering: which products drive revenue, where are we losing customers, and which 3 actions would grow next month's revenue.
Your output: A live dashboard link + a 1-page recommendation + the SQL on GitHub. Add it to your CV with a measurable line.
Side-by-side
A quick comparison if you're still on the fence.
- Core stack
- SQL · Excel · Power BI / Tableau
- Day-to-day energy
- Business questions, dashboards, stakeholder chats
- Proof to ship first
- 1 dashboard + 1-page insight
- Maths heaviness
- Low–medium
- Time to first proof
- ~1–2 weeks
- Core stack
- SQL · Python · dbt · Airflow · Cloud
- Day-to-day energy
- Pipelines, schemas, reliability, code reviews
- Proof to ship first
- 1 scheduled pipeline + README on GitHub
- Maths heaviness
- Low (engineering > maths)
- Time to first proof
- ~2–4 weeks
- Core stack
- Python · Pandas · scikit-learn · Stats
- Day-to-day energy
- Notebooks, experiments, modelling, write-ups
- Proof to ship first
- 1 notebook with baseline + evaluation
- Maths heaviness
- Medium–high (stats matter)
- Time to first proof
- ~2–3 weeks
Still not sure which lane?
The free 2-minute Data Proof Score quiz picks the best-fit pathway for you and shows exactly where your proof gaps are.