We are a managed-models company. We exist because mid-market data teams keep re-solving the same governance, lineage, and semantic-layer problems — and the answer is not another tool, it’s a delivered system.
The board-level argument over what counts as ARR, what counts as a retained account, what counts as an active user — that argument is a definition problem, not a data problem. Certified semantic entities, owned in one place and exposed everywhere, are the only durable answer.
A lineage diagram that lives in a slide deck does nothing. Lineage wired into pre-merge impact scoring is the difference between catching a regression in CI and finding it Monday morning when the CFO opens the wrong dashboard.
For a 6-week governance project, hiring a full data team is the slow path. SOW-bound delivery from senior data engineering, with defined milestones and a clean handoff, ships the same outcome in a fraction of the calendar — and leaves the customer’s team owning it cleanly.
The same revenue model, written from scratch, in three different companies, in three different calendar years — that was the pattern we kept watching. We decided the model layer should be a managed deliverable, not a perpetual rewrite.
The founding team spent a combined eleven years inside VP Data orgs at mid-market SaaS companies. The titles changed, the warehouses changed, the BI tool changed — but the work did not. Every six months, somebody was building the same ARR rollup. Every quarter, somebody was reconciling product-usage definitions between the marketing dashboard and the board deck. Every year, somebody was migrating a tangle of dbt forks into a slightly less tangled one.
The pattern was obvious once we put it down on paper. The hardest part of running a data team was not the warehouse, the orchestrator, or the BI tool. It was the model layer — the certified, governed translation from raw events to a metric the CFO trusts. And every team was rebuilding it alone.
“The model layer is the product. Everything else is plumbing around it.”
So TwiceData is a managed-models company, not a tooling company. We ship dbt model packs, lineage controls, and a governed semantic layer as a delivered system — built on the warehouse you already pay for, wired into the BI tool you already use, owned by your team at handoff.
We work inside the same vocabulary you do: dbt, Snowflake, Databricks, BigQuery, Redshift, Palantir, AWS, Azure. We modernize R and SAS workloads for public-sector and healthcare programs that still run on them, and we move them onto governed cloud data products without breaking the policy and reporting continuity that regulators care about.
None of this is a tool you log into. It is a delivery contract with a definition of done, a lineage gate, and a person who answers the Slack message.
We are a small group of senior engineers with a vetted contractor bench, ready to scale up for any size project — from private-sector SaaS to local and state government engagements.
Every engagement is owned by a senior engineer who designs the architecture and writes the code. No project managers between you and the keyboard. The same person who quotes the work delivers it.
A vetted bench of data engineers, ML specialists, and observability experts gets engaged when the scope demands it — always disclosed in the SOW. You pay for specialist depth only when the work asks for it.
From mid-market SaaS analytics stacks to local and state government data engagements. The engagement scales to the size of the project — Quick Win to Quarter Stack.
Three phases, every engagement, defined in the SOW before anyone writes a model. No discovery month that turns into a discovery quarter.
Map the stack and draft the contract.
Ship the managed model system.
Transfer ownership cleanly.
30-minute architecture session — we'll map your stack to managed model packs and a rollout plan.