Four engagement shapes — what TwiceData looks like in practice.
These are illustrative scenarios showing how each engagement type unfolds, from problem to outcome, with representative metrics. Real client stories will be published here as engagements complete and customers approve named publication. We will not invent a customer to make a sale.
Illustrative scenarios · No real customer-identifying detail · Real engagements added only with explicit consent
Series B fundraise — clean ARR numbers in four weeks.
Problem. A vertical-SaaS company headed into Series B diligence discovered the ARR number on their board deck didn't match the ARR number their Looker dashboards produced, which didn't match what their billing system reported. Three numbers, no source of truth. Diligence two weeks out.
Approach. Stack Diagnostic in week one to inventory the three pipelines and identify the reconciliation gaps. Embedded Delivery Sprint weeks two through four to ship a certified ARR model pack with versioned assumptions, lineage gates wired into CI, and a reconciliation dashboard that all three stakeholders signed.
Outcome. Diligence closed on the updated number. Series B closed at planned valuation. The ARR pack is now the only ARR number used in the company.
R / SAS modernization with full audit continuity.
Problem. A state public-health agency ran their core surveillance reporting pipeline in a tangle of R scripts, SAS jobs, and hand-maintained CSVs. The two engineers who understood it were retiring within 18 months. The reporting cadence (state + federal) couldn't slip a single quarter.
Approach. Quarter Stack engagement (Track A — hand-engineered, given the regulatory posture). 12-week build to port the R/SAS workloads into dbt + BigQuery, preserve every metric's calculation 1:1, and ship a back-test harness proving equivalence quarter-over-quarter against the legacy pipeline.
Outcome. Day-91 handoff completed. Modernized pipeline ran in parallel with legacy for one quarter (zero metric drift). Legacy pipeline retired. New team owns it.
Warehouse spend cut 45% in eight weeks.
Problem. A two-sided marketplace's Snowflake bill was climbing 60% year-over-year on flat usage. The data team suspected duplicated transformations and runaway queries but couldn't prove which ones, where, or how much. The CFO was asking weekly.
Approach. Stack Diagnostic in weeks one through six produced a complexity scorecard, spend-to-value map, and prioritized list of 22 cost-leak candidates. Model Efficiency Upgrade engagement followed (weeks seven through fourteen) to consolidate duplicate ARR model variants, rewrite three slow nightly jobs into incremental builds, and harden the lineage layer.
Outcome. Snowflake spend down 45% within the first full billing month post-handoff. Model count consolidated from 280 to 162. Query queue P95 down 70%.
Full Quarter Stack — one quarter, one source of truth.
Problem. A larger SaaS company had a data team of four engineers and a backlog stretching out 14 months. Sales, support, finance, and product each ran their own ARR variant. Quarterly business reviews regularly stalled because the numbers disagreed.
Approach. Quarter Stack engagement (Track B — Express, given the modern Databricks-native stack and the time pressure). 12-week scope.
Outcome. Day-91 handoff completed. Customer's own data team owns the layer. Subscription maintenance kicked in for ongoing model updates. QBR meetings now end on time.
Want the full case studies under NDA?
Specifics — customer names, dollar values, stack details — available under mutual NDA on request. 30-minute architecture session to walk through whichever case is closest to your situation.