The Studio

About Datawerk

A focused, senior-level BI studio — not an agency, not a consultancy with rotating staff. One person, one methodology, full accountability from design through to ongoing operation.

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Background

Eight Years of Data Infrastructure Work

Datawerk was founded on a simple observation: most growing companies waste significant time and money trying to build data capabilities before they've established what decisions they actually need to make.

The studio draws on eight years of hands-on experience in BI, analytics engineering, and data architecture — across SaaS, consumer apps, e-commerce, and marketplace businesses. The work has ranged from zero-to-one data warehouse builds for seed-stage startups to metric governance projects at Series B companies preparing for board-level scrutiny.

Before founding the studio, the principal worked embedded inside product and growth teams, which shapes the way every engagement is approached: the goal is decision quality, not data volume.

The studio operates as a deliberately small structure. There are no account managers, no project coordinators, no junior analysts learning on client time. Every engagement is handled directly by the principal, from the first scoping call through to ongoing maintenance.

Analytics Engineering Data Warehouse Design Tracking Strategy Metric Governance Dashboard Development dbt BigQuery Snowflake

How We Think

Operating Principles

The beliefs that shape how every engagement is structured, scoped, and delivered.

01

Architecture before implementation

Decisions made during the design phase determine 80% of the outcome. Rushing to code before the data model is agreed costs more time than any deadline justifies.

02

Decisions, not dashboards

The measure of good analytics work is whether it changes what people do. A beautifully designed dashboard that nobody acts on is not a success.

03

Production standards from day one

A data warehouse that works in staging and breaks in production is not a data warehouse. Everything is built to be maintained, extended, and handed over cleanly.

04

Documentation is not optional

If a metric can't be explained in writing, it isn't defined. Every model, every metric, every transformation gets documented as part of the work — not as an afterthought.

05

The right tool for the job

Technology choices follow from requirements. There is no stack that fits every company. Recommending the familiar over the appropriate is a form of negligence.

06

Continuity matters

A data infrastructure that requires the original builder to maintain it indefinitely is not a good outcome either. Everything is built to outlast the engagement.

The Model

How the Studio Operates

A small, deliberate structure designed to deliver senior-level output without the overhead of a traditional agency.

Single point of accountability

The principal handles every engagement directly. There are no junior analysts, no account managers, and no handoffs between team members. You speak to the person doing the work.

Scoped, not open-ended

Every engagement begins with a clearly defined scope and output. There are no vague retainers for "support." What gets built, what it costs, and when it's complete are agreed up front.

AI-augmented delivery

Structured workflows and AI tooling compress the time spent on documentation, boilerplate, and repetitive pattern work. Senior judgment directs the work. Throughput is higher than it was three years ago — without adding headcount.

Embedded for the long term

After implementation, the studio remains available as an embedded BI partner — monitoring, adapting, and extending the infrastructure as the business evolves. This is optional, but most clients choose it.

Fit

Who This Is For

The studio is a good fit for a specific kind of company. Being clear about this saves everyone time.

Funded startups building their first data layer

Seed to Series B companies that have real revenue, real data, and no structured way to understand either of them.

Companies that have outgrown spreadsheets

Businesses where key metrics are calculated in Excel, live in someone's head, or differ depending on who you ask.

Growth teams without analytics infrastructure

Marketing and product teams spending time on reports that should be automated — and making decisions based on data they don't fully trust.

Companies not yet ready to hire a full data team

Businesses that need the output of a data function but don't yet have the size, budget, or management capacity to build one internally.

Probably not a fit if…

You need a team of five with dedicated data science, ML engineering, and an internal CDP implementation. The studio is not the right choice for companies at that scale. It's the right choice for companies that aren't there yet — and want to build correctly from the start.

Start Here

Ready to talk through your setup?

A 30-minute conversation is enough to establish whether there's a good fit. No sales process, no deck — just a direct conversation about what you need.

Book a Free Consultation