Role roadmaps / BI Analyst Role roadmap
Own the serving layer: semantic models, certified datasets, and reports that finance will sign. Closer to the warehouse than a classic analyst.
Also called: BI Developer, Reporting Engineer
You ship: Certified datasets, governed metrics, and reports that survive a refresh.
10–14 weeks if you already write SQL
Roadmap Coding Interview Question bank Switch roleData Analyst BI Analyst Data Engineer Analytics Engineer Data Scientist ML Engineer MLOps Engineer AI Engineer Data Architect Nearby roles:Data Analyst , Analytics Engineer
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Terminal Start · Station 1 of 16
Click to stop Start · BI Analyst Certified datasets, governed metrics, and reports that survive a refresh. Inspect station brief →
Checkpoint · Station 2 of 16
Click to stop Foundations The tools you cannot fake in week one Inspect station brief →
Bus Stop · Station 3 of 16
Click to stop Warehouse SQL Joins at the right grain, slowly changing dimensions, and why fan-out lies. Open Syllabus →
Bus Stop · Station 4 of 16
Click to stop Dimensional modeling Facts, dimensions, and a date table you would defend. Open Syllabus →
Bus Stop · Station 5 of 16
Click to stop Visual grammar Bar vs line, dual axes as a last resort, and color that means something. Open Syllabus →
Checkpoint · Station 6 of 16
Click to stop Daily craft What you actually do on a Tuesday Inspect station brief →
Bus Stop · Station 7 of 16
Click to stop Power BI / Tableau / Looker One semantic model, many reports. Measures live in the model, not in 40 workbooks. Open Syllabus →
Bus Stop · Station 8 of 16
Click to stop Refresh and freshness Incremental refresh, gateway vs cloud, and who gets paged at 7am. Open Syllabus →
Bus Stop · Station 9 of 16
Click to stop Row-level security Who may see which rows. Test it with a real user, not only admin. Open Syllabus →
Checkpoint · Station 10 of 16
Click to stop How you ship Production habits, not extra courses Inspect station brief →
Bus Stop · Station 11 of 16
Click to stop Certification of datasets A published source of truth with tests, owners, and a deprecation path. Open Syllabus →
Bus Stop · Station 12 of 16
Click to stop Report performance Aggregations, DirectQuery vs import, and not pulling the fact table into the visual. Open Syllabus →
Checkpoint · Station 13 of 16
Click to stop Career moves How this role talks in interviews Inspect station brief →
Bus Stop · Station 14 of 16
Click to stop BI interview A broken report, a grain question, and “how would you certify this dataset?” Open Syllabus →
Bus Stop · Station 15 of 16
Click to stop Next role Analytics engineer if you want dbt. Data analyst if you want more narrative. Architect if you want platforms. Open Syllabus →
Final Destination · Station 16 of 16
Click to stop Keep practicing SQL + Python for this role Inspect station brief →
Start · BI Analyst Foundations Warehouse SQL Joins at the right grain, slowly changing dimensions, and why fan-out lies. Dimensional modeling Facts, dimensions, and a date table you would defend. Visual grammar Bar vs line, dual axes as a last resort, and color that means something. Daily craft Power BI / Tableau / Looker One semantic model, many reports. Measures live in the model, not in 40 workbooks. Refresh and freshness Incremental refresh, gateway vs cloud, and who gets paged at 7am. Row-level security Who may see which rows. Test it with a real user, not only admin. How you ship Certification of datasets A published source of truth with tests, owners, and a deprecation path. Report performance Aggregations, DirectQuery vs import, and not pulling the fact table into the visual. Career moves BI interview A broken report, a grain question, and “how would you certify this dataset?” Next role Analytics engineer if you want dbt. Data analyst if you want more narrative. Architect if you want platforms. Keep practicing Phase 1: Foundations Warehouse SQL Joins at the right grain, slowly changing dimensions, and why fan-out lies. Dimensional modeling Facts, dimensions, and a date table you would defend. Visual grammar Bar vs line, dual axes as a last resort, and color that means something. Phase 2: Daily craft Power BI / Tableau / Looker One semantic model, many reports. Measures live in the model, not in 40 workbooks. Refresh and freshness Incremental refresh, gateway vs cloud, and who gets paged at 7am. Row-level security Who may see which rows. Test it with a real user, not only admin. Phase 3: How you ship Certification of datasets A published source of truth with tests, owners, and a deprecation path. Report performance Aggregations, DirectQuery vs import, and not pulling the fact table into the visual. Phase 4: Career moves BI interview A broken report, a grain question, and “how would you certify this dataset?” Next role Analytics engineer if you want dbt. Data analyst if you want more narrative. Architect if you want platforms.
Warehouse SQL Joins at the right grain, slowly changing dimensions, and why fan-out lies.
BI bugs are usually grain bugs wearing a DAX costume.
Checked automatically: you solved the linked practice problems.
BI bugs are usually grain bugs. Join facts to dimensions at the grain the visual claims.
Time to learn Warehouse SQL until fan-out is obvious in a visual.
Prerequisites Grain Why a many-to-many join explodes a bar chart What you need SQL problems SCD article A date table you would defend Concepts to study Star schema query patterns Slowly changing dimensions (Type 1/2) and “as-of” reports Fan-out from a many-to-many bridge Conformed dimensions (date, customer) shared across facts Degenerate dimensions (order_id on the fact) Tech stacks Warehouse SQL — Source of the semantic modeldbt — Where the star is builtPower BI / Looker / Tableau — Where the star is consumedHow to study Draw one star: fact grain, four dimensions, one SCD2 example. Read the SCD guide; write an as-of query for a customer attribute. What to produce A “latest row” query that does not duplicate facts A grain sentence for the primary visual Practice on this site SQL problems; SCD article; ranking/dedup track for “latest row” Pitfalls Joining two facts without a conformed grain Using current customer country for last year’s orders without saying so DAX that hides a fan-out Interview prompts This visual says “revenue by salesperson this quarter.” What is the grain? Links
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