01
Every team has a different number
Finance, ops, growth, and leadership each keep their own definitions. Meetings become reconciliation instead of decisions.
analytics engineering · open source · owned platforms
Blueprintdata designs and builds the data foundations growing teams actually run on: shared metrics, governed models, and open tooling your engineers can own.
the problem
Another dashboard will not settle the argument. What does is shared definitions, consistent models, and access people can actually use.
01
Finance, ops, growth, and leadership each keep their own definitions. Meetings become reconciliation instead of decisions.
02
When every question needs an ad-hoc pull, product slows down and people decide with half the picture.
03
Without a shared model, every dashboard reimplements business rules with slightly different bugs.
04
Copilots amplify whatever sits underneath. Untrusted foundations produce confident nonsense.
proof
services
Named outcomes, not a vague consulting menu. Each engagement ships a foundation your team can operate, with proof you can inspect.
Stand up ingestion, warehouse, governed dbt models, and orchestration from scratch so product, finance, ops, and growth share one analytics layer your engineers can own.
See proof: Takenos case study →Move off warehouse limits and ad-hoc pipelines into IaC, Meltano, dbt, and Airflow with clear conventions so your Data and ML teams stop fighting infrastructure.
See proof: Kiwi case study →Consolidate revenue, transactions, and entity definitions across sources so every team uses the same logic instead of reconciling competing numbers.
See proof: VC deal-flow case study →Layer natural-language and copilots on trusted warehouse logic so AI answers the same definitions dashboards use, not shadow metrics.
See proof: Takenos case study →how engagements run
Visible weekly loops, workshops, and ownership transfer so your engineers extend the foundation after we leave.
tools we work in
open source
Singer taps, templates, and delivery tooling. Strong platforms are repeatable systems, not one-off scripts buried in a client repo.
how we work
Senior teams need more than capacity. They need a model that keeps work visible, aligns people on shared logic, and transfers ownership cleanly.
Start with the decisions that matter. Map shared metrics and the source systems behind them before writing a line of SQL.
No six-month blackouts. Progress stays reviewable, collaborative, and tied to the teams who will live with the outcome.
Governed transforms, docs, and access patterns so dashboards, analysis, and copilots scale on the same logic.
Workshops, code review, and a maintainable dbt workflow. Your team extends the foundation after we leave.
team
We stay close to the work. Commercial context meets hands-on engineering so the output serves both decision-makers and the people who maintain it.

Co-Founder · Data Engineer
Governed metrics, scalable foundations, and AI-ready access for transaction-heavy businesses.

Co-Founder · Data Engineer
Platforms, dbt workflows, and cloud architecture with a bias toward maintainability and ownership.

Data & AI Engineer
Tooling, integrations, and AI-enabled workflows that make analytics reusable and production-ready.

partner
We design and run data platforms on GCP with the same standards we apply to open source: infrastructure as code, reviewable pipelines, and ownership that stays with your team.
Talk GCP with uswriting
faq
We build greenfield data foundations, migrate and standardize fragile stacks, consolidate shared metrics and identity, and enable AI access on governed models. Engagements run as build-and-handoff so your engineers own the system after we leave.
Growing teams where product, finance, ops, growth, and leadership need the same logic. Especially when dashboards, reporting, or AI are scaling faster than the underlying foundation.
We publish Singer taps and delivery tooling because strong platforms are repeatable systems. Public code leaves you with assets that are not locked to a vendor relationship.
Visible weekly cycles, reviewable modeling choices, and a clean handoff. The goal is maintainable workflows and documented logic your engineers own after we leave.
Foundations first. We create governed models and shared metrics, then enable AI-ready access so copilots and NL analytics query trusted logic instead of inventing shadow metrics.
contact
Shared metrics, a warehouse rebuild, open connectors, or AI on a foundation that is not ready yet. Send context. We will reply with a concrete next step.