Career Roadmaps
Pick a role card below to explore the complete roadmap — skills, concepts, tools, practice resources, and interview prep. Each roadmap shows you exactly what to learn, in the right order, with links to hands-on practice on this site.
Foundations
Tools you cannot fake in week one
Daily craft
What Tuesday actually looks like
How you ship
Tests, cost, and on-call habits
Career
The interview loop and the next role
Click any role card to explore the complete roadmap with skills, study guides, and resources.
11 skills · 4 phases
Turn messy tables into answers someone can act on. SQL, spreadsheets, a BI tool, and the nerve to say the number is wrong.
You ship:
Trusted dashboards, ad-hoc SQL, and a written recommendation — not a model.
Time: 8–12 weeks to be useful; months to be trusted
Also called: Business Analyst (data-heavy), Reporting Analyst
10 skills · 4 phases
Own the serving layer: semantic models, certified datasets, and reports that finance will sign. Closer to the warehouse than a classic analyst.
You ship:
Certified datasets, governed metrics, and reports that survive a refresh.
Time: 10–14 weeks if you already write SQL
Also called: BI Developer, Reporting Engineer
12 skills · 4 phases
Move data that other people can trust: ingest, transform, orchestrate, and keep the warehouse bill from becoming a surprise.
You ship:
Pipelines with tests, an on-call story, and a warehouse someone else can query.
Time: 4–6 months to ship independently
Also called: Analytics Platform Engineer, Pipeline Engineer
11 skills · 4 phases
The dbt-shaped role: models, tests, CI, and a metric finance can reuse. Between analyst questions and engineer pipelines.
You ship:
Versioned marts, contracts, and a metric layer someone else can query.
Time: 8–16 weeks if you already write SQL
Also called: Analytics Developer, Metrics Engineer
10 skills · 4 phases
Decide with data: experiments, models, and uncertainty. You still need SQL. You do not need a new architecture every sprint.
You ship:
An experiment, a model with a holdout, and a decision memo — not a demo notebook.
Time: 3–6 months of focused practice beyond SQL
Also called: Applied Scientist, Research Scientist (industry)
10 skills · 4 phases
Train, evaluate, and serve models as systems: features, latency, and rollback. Closer to production than research.
You ship:
A served model with a metric, a rollback, and a feature table that matches training.
Time: After you can train a model locally — then the job starts
Also called: Applied ML Engineer, ML Platform Engineer (IC)
10 skills · 4 phases
Make models a boring production service: CI, registries, feature stores, and the pager. Most of the pain is data platform pain.
You ship:
A path from commit to served model with rollback, plus the monitors that catch drift.
Time: After you have operated pipelines — add the model lifecycle
Also called: ML Platform Engineer, AI Platform Engineer
10 skills · 4 phases
Ship products on pretrained models: retrieval, eval, and guardrails. The hard parts are data, not the model brand.
You ship:
A retrieval pipeline with evals, access control, and a rollback — not a chat demo.
Time: Faster to demo, slower to production than people admit
Also called: Applied AI Engineer, LLM Application Engineer
10 skills · 4 phases
Choose contracts: grain, ownership, lake vs warehouse, and what you will not build. You still need to have shipped.
You ship:
A platform sketch the org can fund, plus the standards that keep it from rotting.
Time: Years of shipping first. This is not an entry role.
Also called: Analytics Architect, Platform Architect
Thinking of switching? Put two roadmaps side by side to see the skills that carry over, the ones you still need, and how long each role takes.Compare Data Analyst and Data Engineer, or pick any pair.
The same nine roles appear incoding problemsandinterview tracks— 9 roles, one catalog.