From Manual Spreadsheets to Self-Serve Analytics: A SaaS Data Engineering Build
A 7-week modern data stack eliminated 56 hours/month of manual reporting, cut month-end close from 5 days to 2, and preserved $186K in ARR.
The company — a project management SaaS used by 1,200+ companies, growing 40% year-over-year — generated data across 7 systems: HubSpot, Stripe, a PostgreSQL production database, Intercom, Google Analytics, QuickBooks, and Gusto. Answering basic business questions took 2 analysts days of spreadsheet work.
Computing Net Revenue Retention meant exporting from Stripe, HubSpot, and the product database, reconciling mismatched customer IDs with VLOOKUPs, and producing a number nobody fully trusted. The CEO wanted a Monday-morning dashboard showing ARR, NRR, churn, expansion, and usage trends with no analyst intervention. The VP of Finance was burning 5 days on month-end close because financial reporting required manual aggregation from 4 systems.
A 1-week, $4,000 data assessment audited all 7 sources and prioritized the reporting needs — the ARR/MRR dashboard alone cost 4 manual hours weekly, NRR 8 hours monthly, and the financial close pack 20 hours monthly. The plan: ingest everything into Snowflake, model metrics in dbt, serve self-serve dashboards in Metabase.
Weeks 2–3 stood up the infrastructure. Fivetran connected all 7 sources with sync schedules matched to need — hourly for product usage, every 6 hours for Stripe and HubSpot, daily for GA and QuickBooks. Snowflake used a three-layer architecture: an immutable raw layer as audit trail, a staging layer with customer IDs unified across systems, and business-ready marts for finance, customer, product, and support domains.
Weeks 4–5 built 34 dbt models — MRR per customer with proration logic, an ARR waterfall, cohort-based NRR, a composite customer health score, and feature adoption models — each guarded by data quality tests so corrupt source data fails the pipeline loudly instead of producing wrong metrics. Weeks 6–7 shipped 6 Metabase dashboards (executive, financial close, customer health, product analytics, support, marketing) and trained the teams in a 2-hour session.
Manual reporting fell from 56+ hours a month to 4 hours of review (-93%). Month-end close dropped from 5 days to 2 (-60%). Time to answer a business question went from 4–8 hours of manual analysis to 30 seconds on a dashboard. All 7 data sources now live in Snowflake as a single source of truth, with real-time health scoring across all 1,200 customers.
The health score dashboard drove proactive saves: the customer success team identified 47 accounts trending toward churn and retained 31 (66% save rate), preserving approximately $186,000 in ARR. Product analytics revealed that time tracking — an underused feature — correlated 2.4x more strongly with retention than any other feature, redirecting the next release cycle.
Year 1 investment totaled $78,200 ($44,000 build, $850/month platform costs, $2,000/month support retainer) against $270,000+ in Year 1 value from analyst time saved and churn prevention.
Before Gigabit, every Monday started with 'does anyone have the latest numbers?' Now every Monday starts with 'here's what the numbers are telling us.' That shift — from gathering data to acting on data — is worth more than the time savings alone.
