Role roadmaps / BI Analyst Role flowchart
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
Checked skills
0% · click a node for concepts, stacks, access, and what to produce · drag to pan · Ctrl-scroll to zoom
− Fit + Clear checks
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 Study this node
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.
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
Mark as done All Articles Stacks Practice Learn Pages
No results. Try analyst, numpy, snowflake, or roadmap.
↑↓ navigate↵ openesc closeAliases: sf, numpy, analyst, mlops
