Data Engineering for Healthtech.
Compliant, Reliable, Built to Last.
HIPAA-compliant pipelines. Reliable metrics. Data your whole team trusts.
Most data engineers build first and worry about compliance later. In healthtech, that approach creates expensive rework — or worse, violations. We architect for HIPAA, GDPR, CCPA, and LGPD from the start: data sensitivity classification, audit trails, role-based access controls, and retention policies are built into the foundation, not patched on after the fact.
Last updated: June 2026
12
People across the company now use infrastructure we built from scratch (based on FDE client engagement)
11
Non-technical users trained on self-serve analytics in hands-on sessions (based on FDE client engagement)
$10.93M
Average cost of a healthcare data breach, the highest of any industry (IBM/Ponemon, 2024)
10h
Per week given back to the CEO by eliminating manual data validation (based on FDE client engagement)
Is This You?
This is for you if
- You are a healthtech startup with no data infrastructure and no data person on the team
- Your CEO or clinical lead is manually pulling and validating data because there is no other option
- You need HIPAA-compliant data pipelines but cannot justify a full-time senior data engineer
- You have data but only one person can access it — the rest of the team is flying blind
- You need your data foundation ready for regulatory reporting, investor due diligence, or AI initiatives
Not the right fit if
- You need a full-time embedded engineer building clinical features in your product
- Your primary need is data science or ML model training on clinical data
- You need real-time patient monitoring systems — we build analytical infrastructure, not clinical systems
- You cannot provide data access due to organizational or compliance restrictions
Why Is Data Engineering Different for Healthtech Companies?
Health data carries higher stakes than most industries. A wrong number in a dashboard is not just an inconvenience — it can affect care decisions, compliance filings, and the trust your team puts in the data. We build for reliability first.
Compliance Is the Foundation, Not an Afterthought
Every healthtech engagement starts with compliance. We classify data sensitivity, set up appropriate access controls, handle retention and deletion policies, and build audit trails. HIPAA, GDPR, CCPA — the regulations that apply to your data are baked into the architecture from the first day, not patched in later.
Data Quality You Can Trust
We build automated data quality checks that run continuously. When we built a dbt foundation for a healthtech startup, we uncovered data quality issues that had been silently distorting their metrics. Every issue was documented, traced to its root cause, and resolved. Their CEO went from manually cross-checking AI-generated SQL queries to trusting the data completely.
Self-Serve Analytics for Non-Technical Teams
In healthcare, the people who need data the most — clinical leads, operations managers, C-level executives — often cannot write SQL. We set up self-serve analytics tools like Metabase and run hands-on workshops with the actual data your teams work with daily. For one healthtech client, clinical leads and operations managers were pulling their own reports within weeks — no more waiting on someone with SQL access.
Lean Stacks That Scale
Healthtech startups need to be capital-efficient. We design data infrastructure that works on lean budgets — open-source tools where possible, free tiers where appropriate, and architecture decisions that keep costs low without sacrificing reliability. One client runs on the free tier of dbt Cloud and the basic plan of Metabase. No unnecessary spend.
How Does a Healthtech Data Engineering Engagement Work?
Every engagement starts with understanding your compliance landscape and your business. We build from there.
Compliance and data audit
We identify which regulations apply, classify your data sensitivity, and design the architecture to meet those requirements from the start.
Build the data foundation
Data models mapped to your business, automated quality checks, and full documentation. Your team gets infrastructure they can trust and build on.
Enable your team
Self-serve analytics, training sessions, and complete documentation. Your team can operate independently — or we can stay on for ongoing maintenance like compliance updates, new data source integrations, and pipeline monitoring.
Details on our compliance-first approach and each delivery phase are on our homepage.
Healthtech 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.
0 → 1
dbt data foundation built from scratch
10 hrs/week
given back to the CEO by eliminating manual data validation
Automated
data quality checks running continuously
Tech: dbt · dbt Cloud (free tier) · PostgreSQL · Data Modelling · dbt Tests · Calculated Columns · dbt Documentation
Read case study →
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
0 → 1
Metabase built and adopted from scratch
11
non-technical users actively using self-serve analytics
4 departments
onboarded: marketing, sales, operations, C-level
Tech: Metabase · Metabase (basic plan) · dbt · PostgreSQL · Workshop Design · Notion · Data Governance · Self-Serve Analytics
Read case study →
From a Healthtech CEO
“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.”
Let's Build Your Data Foundation
Book a free call. We will review your current data setup, discuss compliance requirements, and outline what a healthtech data foundation looks like for your team.
Currently accepting 1 of 3 new clients
Frequently Asked Questions About Healthtech Data Engineering
Do you build HIPAA-compliant data pipelines?
Yes. We account for HIPAA requirements from the start of every healthtech engagement — data sensitivity classification, access controls, retention policies, and audit trails. Compliance is part of the architecture, not something bolted on after the build. We also handle GDPR, CCPA, and LGPD where applicable.
Can you work with health data stored in our EHR or clinical systems?
We can build pipelines that connect to your clinical data sources as long as we have appropriate access. We handle the data engineering layer — extracting, transforming, and loading data into a compliant warehouse — while respecting the access controls and governance requirements your compliance team sets.
We have no data infrastructure at all. Can you start from scratch?
That is exactly what we did for a healthtech startup with 50 employees. They had no data models, no documentation, and no way to get reliable metrics. We built their entire dbt data foundation from scratch, added automated quality checks, and within 3 months the CEO stopped manually validating data and the whole team was running off the same infrastructure. Read the full case study on our case studies page.
How do you handle data quality in healthcare contexts?
Healthcare data quality directly affects clinical and business decisions. We build automated data quality checks that run continuously — catching issues before they reach reporting. In one healthtech engagement, we identified and resolved data quality issues that had been silently distorting reported metrics for months.
Can non-technical team members access the data?
Absolutely. In one healthtech engagement, the operations team, clinical leads, and the CEO all went from zero analytics access to pulling their own compliance and performance reports. We set up role-based dashboards so each person sees only the data they are authorized to access — which also satisfies HIPAA minimum-necessary requirements.
What happens to data access and documentation when the engagement ends?
Everything we build is yours — including compliance documentation, data sensitivity classifications, access control configurations, and architecture runbooks. We include 30–120 days of post-engagement support depending on your plan. Your compliance officer and your data team both get the documentation they need. If you want ongoing maintenance after the build, we offer that too.