Build executive dashboards, track KPIs, and translate business questions into SQL.
31 missions|5 tables
“Who is hitting their sales targets?”
“Rank the sales team”
“Three-table join: who sold what where?”
Dimensional Modeling in Practice — A 5-table star schema (fact_sales, fact_targets + 3 dimensions) with trailing spaces, ghost regions, inactive employees, and casing drift — the exact patterns you’ll handle modeling real marts.
Window Functions End-to-End — RANK, ROW_NUMBER, LAG, LEAD, NTILE, PERCENT_RANK, SUM() OVER with framed windows, moving averages, YoY pivots, and running totals — the window-function curriculum most interviews screen on.
Grain Reasoning & Fan-Out Bugs — Mission 26 (“Why is our leaderboard off by 12x?”) drills the canonical BI mistake: a clean query, the wrong grain, a 12× inflated total. Pre-aggregated CTEs as the fix.
OpportunityHistory SCD2, Closed-Won reopens, stage regressions, forecast accuracy, ARR waterfalls — the SQL RevOps and Sales Ops analysts actually write.
34 missions|8 tables
“What stages exist in the pipeline?”
“Deals that reopened after Closed-Won”
“Closed-Won cohort retention by month”
Salesforce-Shaped Schema — Account, Contact, Lead, Opportunity, OpportunityHistory, User. The exact table shape you’ll see on day one of any Sales Cloud shop, with the field names you’ll recognize from real Salesforce exports.
OpportunityHistory SCD2 — Every stage change, amount change, owner change logged as a row. Point-in-time reconstruction (“what did the forecast look like on March 15?”), stage regression detection, and the Closed-Won reopen anti-pattern that invalidates 80% of naive pipeline reports.
Sales Cycle + Velocity SQL — Stage-duration windows, rep win-rate by region, top-N per group, quarter-over-quarter bookings waterfalls, forecast accuracy as a backtest. The full RevOps analyst toolkit.