Stay in our lane.
We make data easy and trusted. That's it. Analytics, dashboards, business decisions — those are your team's job, and they should stay that way.
About DataKitchen
A small senior team. A platform built for pharma. A decade of commercial launches behind us.
DataKitchen has been engineering reliable, analyst-ready commercial pharma data since the Celgene days. The team is bootstrapped, fully remote, and unusually opinionated about how data engineering should work for a pharma analytics organization.
At a glance
The shape of the practice.
- Founded
- 2013, Cambridge, MA. Bootstrapped, fully remote.
- Practice focus
- Tech-enabled commercial pharma data engineering — analyst-ready data, automated quality, transparent operation.
- Customer outcomes
- $100B in acquisition value across Karuna (Cobenfy), Celgene (Otezla/Revlimid), and Acceleron.
- How we deliver
- A small senior team backed by DataKitchen's own DataOps software — orchestration, AI-assisted test generation, and full observability.
What we believe
Four convictions that shape the work.
No black boxes.
Every line of code, every test, every schema, every dashboard configuration lives in your cloud and your repositories. When your IT team is ready to take over, the handoff is a normal day.
Specialized beats generalist.
Generalist IT is the wrong shape for commercial pharma data. Our engineers know NPP, payer claims, specialty pharmacy, syndicated data, and Veeva on day one.
Speed comes from automation, not heroics.
Our DataOps platform generates and runs the tests, orchestrates the deployments, and catches errors before they reach your analysts. Small senior team, large output.
Leadership
The people you'll meet.
Same founders, same engineering bar since 2013. When you book an intro call, you talk to one of the five people below — not an account executive.
Chris Bergh
CEO & Head Chef
The "Head Chef." Co-founder and CEO. Takes the first call on a new launch himself. Leads the DataOps movement; co-authored the DataOps Cookbook and the DataOps Manifesto. Background: MIT Lincoln Lab, NASA Ames.
LinkedIn
Eric Estabrooks
Co-Founder & VP of Services
Co-founder and VP of Services. 25+ years leading data services and software teams. The senior engineer behind most of our pharma launches; the person you're likely to meet on a Tuesday status call.
LinkedIn
Gil Benghiat
Co-Founder & VP of Products & Implementation
Co-founder and VP of Products & Implementation. 35+ years in software engineering, including AT&T Bell Labs and Sybase. Owns the commercial-pharma practice and stays close to every active engagement.
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Chip Bloche
Director of Data Engineering and Quality Solutions
VP of Data Engineering. 30+ years on OLTP, warehousing, and analyst-ready data design, and the principal architect of our open-source data quality testing tool. Sets the engineering bar across every pharma platform we ship.
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Aarthy Adityan
VP of Software Engineering
VP of Software Engineering. Leads the dev team and owns IT, UX, and DevOps, so the cloud setup and access model your security review asks about are hers. Background: MIT, Northeastern, IIT Madras.
LinkedInQuestions
About the team and the practice.
How long has DataKitchen done this?
Since 2013, and commercial pharma launches since the Celgene days. The company was founded in Cambridge, MA, is bootstrapped, and is fully remote. The founders co-authored the DataOps Cookbook and the DataOps Manifesto, so the methods behind the work are published rather than proprietary.
Who will we actually work with?
Our founders, not an account executive. Chris Bergh, our CEO, takes the first call on a new launch himself. Eric Estabrooks is the senior engineer behind most of our pharma launches and the person you are likely to meet on a Tuesday status call. Gil Benghiat owns the practice, and Chip Bloche sets the engineering bar on every platform we ship.
How many engineers do you put on a launch?
One to three, typically. Roughly 1.5 engineers built the entire Cobenfy launch data platform at Karuna over two years, work most pharma companies staff with 10 engineers. At Celgene, seven of our engineers supported a $10 billion-a-year commercial portfolio. Small senior teams backed by automation, not a bench of generalists.
Do your engineers know pharma data on day one?
Yes. Our engineers know NPP, payer claims, specialty pharmacy, syndicated data, and Veeva before they start. A generalist IT team can learn those nuances eventually, but the learning happens on your launch timeline. Nobody spends the first three months finding out what a specialty pharmacy feed is.
How is this different from ZS, Beghou, or Accenture?
Analytics consultants sell analyst hours, billed at around $300 each, and hand you decks. Accenture and Deloitte bring a 30-person engineering team and a statement of work. We send one to three engineers who write code in your repository. The endgame is different too: they keep the work, we hand it over.
What do you not do?
We don't do incentive compensation, territory alignment, call planning, or brand strategy. We build the commercial data layer underneath all of them. Analytics, dashboards, and business decisions stay with your team, because that is where they belong. Staying in one lane is why a small team can move fast on it.
Ready to see what an embedded commercial pharma data team looks like in practice?
Half an hour with the team. Bring your stack, your launch window, and your hardest analytics question. We'll do the rest.