Write the variance bridges, P&L rollups, and cash-burn queries tech FP&A teams gatekeep on.
30 missions|5 tables
“Cost-center ownership map”
“March budget-variance check”
“YoY revenue growth by month”
Finance Deliverable Scenarios — Answer questions from the CFO, FP&A Manager, Treasury Analyst, Controller, and Senior Auditor — MBR variance packs, budget attainment, revenue seasonality, cash burn, and vendor concentration.
Messy Ledger Data — Duplicate vendors, un-posted journal entries, inconsistent account-type casing, refund leakage, and duplicate payments — the reconciliation traps that quietly wreck a variance narrative.
Finance Analyst SQL Skills — A vs B vs F vs PY variance, gross margin decomposition, running cash burn, YoY revenue growth, percentile ranking, cohort onboarding, and late-posting analysis.
Structuring detection, velocity windows, entity resolution, and rule-tuning backtests — the SQL AML analysts and fraud strategists actually write.
30 missions|6 tables
“What channels do we see?”
“Round-dollar wires — flag a pattern”
“Alert precision — how good is our rule?”
Real AML Typologies — Structuring below the $10K CTR threshold, smurfing across multiple accounts, round-dollar wire cycles, dormant-then-active money mules, and device/IP/email reuse across “separate” customers — every FinCEN / FATF / Wolfsberg pattern maps to a mission.
Velocity + Self-Join SQL — COUNT() OVER with date-range window frames, correlated subqueries for same-counterparty pairs, SOUNDEX / LEVENSHTEIN for entity resolution, and gap-and-island logic for dormant account reactivation.
Rule Tuning + Backtesting — Backtest a proposed threshold against historical alerts, compute rule precision / recall on true-positive labels, and measure false-positive reduction — the analysis every compliance team asks of new hires.
The retail-banking analyst role at a regional or money-center bank: deposit growth decomposition, deposit beta on MMAs, NSF/OD fee revenue post-CFPB, branch consolidation scoring, NII decomposition, primary-banking-relationship classification, churn leading indicators, and FTP-allocated branch P&Ls against a realistic retail bank with planted reconciliation traps.
30 missions|11 tables
“Product taxonomy inventory”
“Deposit beta on MMAs (cycle-to-date)”
“2025 closed-account exit cohort with tenure”
Retail Banking Scenarios — Answer questions from the Retail COO, Branch Ops Manager, Treasurer, and Customer Insights Lead — weekly retail dashboards, ALCO-ready deposit beta, NSF/OD scenario modeling, and branch P&L with FTP allocation.
Authentic Retail Bank Schema — 6-table retail_* core (customers, accounts, products, branches, transactions, daily balances) plus retailops_* specialty tables for branch activity, fees, and ATM events. Numbers anchor to 2024-2026 retail banking realities.
Deposit + Branch + Treasury + Customer Skills — Average daily balance, weighted deposit beta, primary-relationship EXISTS logic, churn lag features, FTP curve allocation, and the multi-CTE rollups that real retail-bank dashboards run on.
The consumer-lending analyst role at a regional or money-center bank: HMDA action-taken decomposition, fair-lending disparity analysis, vintage curves and first-payment-default cohorts, ARM reset wall identification, refi candidate targeting, delinquency aging and roll rates, charge-off recovery, CECL-style loss reserve decomposition, and ALCO-ready portfolio stratification — against a realistic mortgage / auto / HELOC / personal lending dataset with planted HMDA and servicing reconciliation traps.
30 missions|8 tables
“Loan product taxonomy”
“Approval rate by FICO band”
“Refi candidate cohort”
Consumer Lending Scenarios — Answer questions from the Chief Credit Officer, Mortgage Sales Director, Fair Lending Compliance Officer, and Loan Servicing Lead — weekly origination dashboards, HMDA LAR reconstructions, ARM reset walls, and CECL-style loss reserves.
Authentic Lending Schema — lending_applications (HMDA-shaped), lending_originations (funded loans with FICO/DTI/LTV at orig), lending_payments (past installments with days_late), and lending_geography (census tract + LMI). Joins to the shared retail_* core for customer demographics.
HMDA + Vintage + Servicing Skills — HMDA action-taken decomposition, denial-reason rollups, LMI-tract approval gap analysis, vintage curves with cohort EXISTS, ARM reset arithmetic, roll-rate matrices, charge-off recovery ratios, and the multi-CTE rollups that real credit teams ship.