If any of these come up in your weekly standups, you're ready for us.
Only the engineering team can pull data
Each team has different numbers for the same metric
We track events after launching features, not before
We're paying too much for Mixpanel and getting less insight
We want to do ML/AI but our data is a mess
We need to hire a data person but don't know what role
What we do
The full data stack, or the part you need.
We scope every engagement around what you actually need at your stage — not what you'll need in three years.
Data Strategy & Advisory
Define the metrics framework, architecture, and tooling decisions before writing a single line of code. One source of truth starts here.
Founders who know they need data but don't know where to start
Data Infrastructure Build
Cloud setup to production-grade warehouse. Pipelines, event tracking, data source unification, monitoring. Your engineers stop writing manual SQL.
Startups scaling faster than their current data setup
BI & Reporting
Dashboards the whole company actually uses. Investor reporting, funnel analysis, attribution — built on the foundation, not on top of Sheets.
Marketing and finance teams still living in spreadsheets
ML & AI-Ready Infrastructure
Feature stores, model productionalization, vector pipelines, LLM tooling. We build the data foundation that makes AI features possible.
Startups that want to ship AI but their data is a mess
Team Building & Handoff
We define the role, screen candidates, and onboard the person who takes over. Every engagement ends with your team owning the stack.
Every client — this is how engagements end
The model
We build it. We document it. We leave.
Engagements run 3–6 months. We're temporary by design. Our success is measured by how little you need us once we're done.
1
Assess & scope
We start with a data maturity assessment — your current stack, your growth stage, the metrics your board actually asks for. We scope the right work, not the maximum work.
2
Build & ship
We build at startup speed. Pipelines in production, dashboards your team uses, reporting that passes due diligence. No prototypes, no handwavy architectures.
3
Document & hand off
We write the runbooks, help you hire the data engineer, and onboard them properly. Then we're done. That's the goal — not a retainer you didn't ask for.
The team
Small team. Senior people.
Data without finance context is guesswork. We cover both.
AM
Adrian Marruedo
Founder & Partner · Data Infrastructure
Prague · Remote
Data engineer and strategist. Built data infrastructure from zero at multiple funded startups across Europe — first pipeline through Series A reporting. Founded Faro Data to give early-stage companies access to senior data expertise without the full-time hire.
Data infrastructurePipelines & warehousingBI & reportingAI-ready stacks
TB
Tjark Block
Partner · Finance & Operations
Prague · Remote · DACH
Finance & Operations Advisor with 5+ years working with e-commerce and SaaS founders across DACH. Trained as a Steuerfachangestellter (accounting-trained), co-founded an Amazon FBA business. Covers financial clarity, operational restructuring, and DD support — the numbers side when data and finance need to move together.
Financial clarityE-commerceSaaS metricsOperational financeDD & transaction support
Selected work
Data foundations, built to hand off.
Every engagement here ended with the client owning the stack.
Built Goin's entire data function from zero through their Series A.
Joined as employee #1 and stood up the full data stack — pipelines, warehouse, BI, and ML in production for churn — alongside the reporting that supported the Series A round.
25→6 min
Time-to-first-response
+3pp
Funnel conversion
Series A
Due diligence passed
"Adrian was employee #1 at Goin and built our entire data function solo — strategy, pipelines, BI, ML, the lot. One of the most multi-disciplinary people I've ever worked with. Our investors kept bringing up the reporting as a reason they backed us."
— David Riudor, Founder & CEO, Goin