Build Your Data Foundation.
Stop Searching, Start Knowing.
Five tools. Scattered numbers. Manual reports. We consolidate data from multiple sources into one place.
100% Guarantee. Good delivery shouldn't need convincing, so we guarantee it. No risk on your end: at any point, request your last 30 days of payments back.
Right now, someone on your team, probably a founder or an ops lead, is the human API between your tools. They know which Salesforce report to pull, how to export from QuickBooks, and which spreadsheet has the real numbers. A data foundation replaces that person-dependency with automated pipelines that connect your systems, clean the data, and deliver it to a warehouse anyone on the team can query.
Last updated: June 2026
What a Data Foundation Changes
15h
Per week your team stops spending pulling numbers from different tools by hand (based on FDE client engagement data)
1
Source of truth instead of 5 disconnected systems with conflicting numbers
130+
Automated pipelines running reliably in our largest data foundation build (based on FDE client engagement data)
40%
Under budget on a complete data platform for a 500-person global organization (based on FDE client engagement data)
Is This You?
This is for you if
- ✓Your company has data in 3+ systems that do not talk to each other
- ✓People across your team spend hours every week searching for data or reconciling numbers
- ✓Reporting is manual: someone pulls from multiple tools and pastes into a spreadsheet
- ✓Leadership cannot get reliable operational metrics without asking specific people
- ✓You want to make data-driven decisions but the data is too scattered and unreliable to trust
Not the right fit if
- ✕You need a full-time embedded engineer building product features. We build internal data infrastructure
- ✕Your primary need is data science, ML models, or predictive analytics before any foundation exists
- ✕You have a mature data team and established pipelines. You may need a data platform engineer, not a foundation build
- ✕Your data lives in one system and a spreadsheet is genuinely enough for your current scale
What Problems Does a Data Foundation Solve?
The problem is not that your team lacks data; it is that the data is scattered, inconsistent, and hard to reach. A data foundation fixes the plumbing so your team can focus on the work that matters.
A Single Source of Truth, Not Five
We connect your CRM, product database, financial tools, marketing platforms, and operational systems into a single central warehouse. Your team stops asking “which number is right” because there is only one answer, pulled from the same clean, automated pipeline.
Automated Reporting, Not Manual Pulls
If someone on your team is spending hours every week pulling data from multiple tools and pasting it into a spreadsheet, that is the problem we solve. We automate reporting end to end: pipelines run daily, data quality checks catch issues before they reach reporting, and dashboards update themselves.
Self-Serve Analytics for Everyone
Your operations lead, marketing manager, and CEO should not need to ask someone else to pull their numbers. We set up self-serve analytics tools and train your team to use them. After one build, the marketing lead, VP of ops, and the CEO were all pulling their own numbers in Metabase, without asking engineering for anything.
Built to Last Without a Data Team
We build infrastructure your team can maintain independently. Full documentation, data dictionaries, and runbooks. Reusable patterns that make adding new data sources straightforward. When you are ready to hire your first data person, they can pick up exactly where we left off.
How Is a Data Foundation Built?
Map your systems and data needs
Build pipelines and the central warehouse
Enable your team and hand off
Data Foundation Case Studies
Building a dbt Data Foundation from Scratch: A Case Study for Startups Without a Data Team
Health tech · ~50 employees · No data person, just a CEO, AI, and a lot of manual checks. Now 12 people rely on the same infrastructure.
Building a Self-Serve Analytics Culture with Metabase: A Case Study for Startups Without a Data Team
Health tech · ~50 employees · 11 non-technical users now making decisions from data, without asking anyone for help
Building a Data Lakehouse from Scratch on AWS: A Case Study for Complex Organizations
Multinational organization · ~500 employees · $32M revenue · No prior data infrastructure
From a CEO Who Built from Scratch
“They structured our dbt, reduced platform costs, and left documentation so thorough our team kept building on it. No dependencies, no technical debts. More than a one-time delivery, it became the foundation for reliable metrics and data-driven decisions we're still evolving today. Professional, collaborative, and genuinely focused on long-term value.”
Victor G.
CEO at ezaligner
Let's Build Your Data Foundation
Book a free call. We will review your current data landscape, identify what needs to be connected, and outline what a centralized data foundation looks like for your company.
Frequently Asked Questions About Building a Data Foundation
What does 'building a data foundation' actually mean?
It means connecting the systems your company already uses (your CRM, product database, financial tools, marketing platforms) into a single, reliable source of truth. We build automated pipelines that pull data from each source, clean and structure it, and deliver it to a central warehouse where your team can access it without writing code or chasing down numbers across five different tools.
How many data sources can you integrate?
There is no fixed limit. Our largest engagement consolidated data from five sources (PostgreSQL, Salesforce, Freshdesk, Google Analytics, and SparkPost) into a single data lakehouse with 130+ automated pipelines. Most companies start with 3 to 5 sources. The architecture we build is designed to make adding new sources straightforward, so you are not starting over when you adopt a new tool.
Do we need a data team to maintain this after you build it?
No. We design specifically for companies that do not have, and may not need, a dedicated data team. Everything is documented: architecture decisions, data dictionaries, and operational runbooks. One client with no data person has 12 people across the company using the infrastructure we built. If you do hire a data person later, they can pick it up and extend it immediately.
How long does it take to build a data foundation?
Most foundations take 2 to 4 months of focused build work, depending on how many systems you need to integrate and how complex the data is. Across our engagements, the average time to a working data foundation is 2–4 months. After the build phase, maintenance drops to a few hours per week. We built a complete dbt data foundation from scratch in under 3 months for a client with no existing infrastructure.
What tools and technologies do you use?
We choose the right tools for your scale and budget. Common stack components include dbt for data transformation, PostgreSQL or BigQuery for the warehouse, Apache Airflow for pipeline orchestration, and Metabase for self-serve analytics. We favor open-source and free tiers where appropriate. One client runs on the free tier of dbt Cloud and basic Metabase. No vendor lock-in, no unnecessary spend.
Our team is not technical. Will they be able to use the data?
That is the point. We set up self-serve analytics tools and run hands-on training sessions using your real data, not generic demos. People who have never touched a BI tool learn to pull their own reports in Metabase within a few sessions. Your team accesses dashboards and pre-built reports, not SQL queries.