Most growing companies don’t have a data infrastructure problem. They have a data trust problem.
The dashboard says one number, the warehouse says another, and nobody can say which is right — or who changed the logic last quarter, or who can see the rows underneath. The pipeline that feeds the board deck is a notebook on someone’s laptop. The “single source of truth” has three different definitions of active customer. None of this shows up as an outage. It shows up as hesitation — the slow, corrosive sense that you can’t quite stand behind your own numbers.
We started TwiceData LLC to fix that, specifically for the size of company where “just hire a platform team” isn’t an answer.
After years of building governed analytics stacks for growing data teams, we’re formalizing the work: an engineering studio focused on one thing — shipping mid-market data infrastructure that holds up under audit, scales under growth, and doesn’t require a six-person platform team to maintain.
The wall every growing data team hits
There’s a predictable point in a growing company’s life — often somewhere between Series A and Series B — where the data setup that got you here stops carrying you. It tends to look like this:
- The metric that was “fine last quarter” can’t be reproduced, because the logic lived in a dashboard someone has since edited.
- Every model change breaks something three steps downstream, and nobody finds out until a stakeholder does.
- A SOC 2 audit or a customer security review asks “who has access to what, and how do you know?” — and the honest answer is a shrug.
- Finance and Product each have their own number for the same thing, and reconciling them is now a recurring meeting.
None of these are tooling gaps. The company usually has a warehouse, a BI tool, maybe even dbt. They’re discipline gaps — the wiring was done fast, under pressure, to answer a question that was urgent at the time, and it was never built to hold.
Governed data shouldn’t need a platform team
The enterprise answer to all of this is headcount: a dedicated data-platform team, a catalog vendor, a governance committee, a quarter of process. A 2-to-8-person data team at a Series A can’t staff that and keep shipping — so governance gets deferred until something breaks in public, and then it’s a fire drill.
It doesn’t have to be that way anymore. The building blocks finally exist to get audit-holding governance without the headcount — dbt and model contracts for tested, version-controlled logic, OpenLineage for automatic lineage, warehouse-native access control, and an open catalog like DataHub or OpenMetadata. Wired together in the right order, they make one clean path from raw source to board deck:
The hard part was never the tools. It’s wiring them together correctly the first time — in the right order, with the right boundaries — so the system holds instead of rotting the month after you turn it on. That wiring discipline is the entire job, and it’s what we do.
What “governed” actually means
“Governance” is an overloaded word. When we use it, we mean five concrete, testable properties — not a policy PDF:
The same query returns the same number tomorrow.
Every figure traces back through every transformation to its raw source. If you can't reproduce yesterday's board metric, nothing else on this list matters.
Every field has a provenance you can show an auditor.
Source, transformations, the dashboard tile — the whole chain. When something looks wrong, you find the cause in minutes, not days.
Row- and column-level access enforced in the warehouse.
It holds no matter which tool does the querying. Access decisions don't live in five different BI permission screens that quietly drift apart.
Assumptions are encoded as tests that run in CI.
Uniqueness, not-null, referential integrity, accepted ranges. A broken assumption fails the build, not the board deck.
We wrote up the 2026 state of this in our data-validation playbook.
The shape of data in and out is pinned.
An upstream change that would break a contract is caught at the door — not in production, three steps downstream, on a Friday.
A stack with those five properties is one you can stand behind — in a board meeting, in a security review, or at 2am when a number looks off.
What we do
Three tracks, sized to how much help you actually need:
Whatever the track, the deliverable is the same kind of thing: a system your team can run, not a dependency on ours.
Who we’re for
Seed to Series B companies that have hit the wall above. Most have a small data team — usually 2-8 people — who need senior hands and discipline faster than they can hire it. Some have no data team at all: a founder or a couple of engineers carrying the data work on the side, while real revenue decisions ride on numbers nobody has had time to make trustworthy. We work with both.
If you can describe the pain in one sentence, you’re in the right neighborhood:
- “We can’t reproduce yesterday’s board metric.”
- “Every model rebuild breaks something downstream and we hear about it from Slack.”
- “Compliance is asking who has access to what and we don’t have a clean answer.”
- “Nobody actually owns our data, and it’s starting to cost us.”
No data team yet? That’s a fit, not a disqualifier. We can build the foundation from the ground up — the tracking and ingestion layer that captures what happens in your product, the pipelines that move it, and the tested models on top — so your first data hire inherits a clean, documented system instead of the tangle most people walk into. An Embedded Sprint is simply us being your data team for a few focused weeks.
And while mid-market SaaS is our home turf, the same discipline applies to anyone whose decisions ride on data they can’t yet trust — a SaaS product, a marketplace, an operations-heavy business, or something more. If you generate data and need to trust it, we can build the infrastructure to track it, ingest it, and stand behind it.
Some of the best fits are the earliest. If you need pipelines and trustworthy data from day one, or you’re ready to break out of Excel and Google Sheets before a spreadsheet quietly becomes the system the whole business runs on — that’s squarely us. And you don’t start from a blank repo: we bring proven, governed data models and adapt them to your business, so you have working pipelines in days and iterate from there, instead of rebuilding the warehouse from scratch.
The one place we’re probably not your best fit is a large enterprise with a mature platform org already — and we’ll say so on the first call rather than sell you a quarter you don’t need.
How we work
We’re senior, hands-on, and we leave things better documented than we found them. A few principles show up in every engagement:
- Read before we write. Understand what’s there — the warehouse, the models, the access model, the tribal knowledge — before touching anything.
- Smallest change that holds. No speculative platforms, no rip-and-replace for its own sake. We fix the thing, correctly, and stop.
- Everything in version control, everything tested. If it isn’t in git with a test, it didn’t ship.
- You own the outcome. Runbooks, documentation, and lineage are part of the deliverable, not an afterthought — so the day we leave, nothing breaks.
What “done” looks like
We don’t leave you dependent on us. Every engagement is built to outlive the engagement:
- Metrics you can reproduce — the same number tomorrow, traceable to its source.
- Lineage you can show an auditor — every field, where it came from, what touched it.
- Tests that fail loudly — broken assumptions surface in CI, not in a board deck.
- Docs that outlive us — the system is yours to run without a standing retainer.
Why now
The tools have caught up. dbt is mature, OpenLineage is real, the open-catalog space is finally usable, and engines like DuckDB and table formats like Apache Iceberg make warehouse-grade work runnable without warehouse-grade budgets or teams. What’s still missing — the thing no tool ships — is the discipline to wire it together correctly the first time. That’s the part we do, and it’s why a studio our size can deliver what used to take a platform org.
Building for the EU
We’re building TwiceData to serve EU-based companies, not just US ones — and we’re putting the legal groundwork in place to do it properly: Data Privacy Framework alignment, a standard Article 28 data-processing agreement, and an Italian partita IVA for cross-border B2B work. That setup is underway now. Cross-border data work carries real governance weight, and we’d rather get the posture right before we take it on than claim a readiness we don’t have yet.
What’s next
A few things you can expect from us over the coming weeks:
- Engineering posts on the Labs side — actual artifacts: code, configs, decision logs, and the trade-offs behind them.
- Short company updates here on the Blog when something’s genuinely worth announcing.
- An RSS feed once we’re past three posts.
If any of this resonates, the first hour of consultation is free — no deck, no pitch, just a senior data engineer and your actual problem. The door is open.
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