About CloverDX

We help clients build and manage better data pipelines, with the CloverDX Data Management Platform and our in-house team of data experts.

Founded in 2007, we now have offices in the US, UK and Czech Republic, and work with some of the world’s leading companies in industries such as finance, healthcare and consulting.

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Our Story

Back in the early 2000’s, during the dawn of the open-source movement, CloverDX began as a team of data consultants. But existing data platforms were expensive, and we needed a tool that would let us work faster and get more control over data pipelines. So we decided to ‘scratch our own itch’, and built our own.

Originally known as clover.ETL, our internal tool gave us a robust, reliable, flexible data platform. Soon, we realized that our tool would be useful for others too.

Over the years, we’ve seen the software industry’s hype, broken promises and false dawns. Companies have spent vast sums on data tools without getting the results they wanted. It has prompted commentators like Nicholas Carr to ask: 'does IT even matter?'

We think it does matter. Very much.

We’ve built CloverDX on practical outcomes, tangible results and ongoing innovation – all with the aim of helping organizations focus on the most important part of data - connecting the dots and bringing it all together.

Today we focus on helping our clients solve their most complex data problems. We aim to build the best possible data management platform. And we want to bring better ways of handling data to organizations around the world.

Ready to take the next step?

Book a meeting with one of our data experts and find out how CloverDX can help you overcome your specific data challenges.

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Leadership Team

David Pavlis

David Pavlis

CEO, Founder

Michal Tomčányi

Michal Tomčányi

CTO (likes his team more than photos)

Jiří Vojtek

Jiří Vojtek

Chief Operations Officer

Alex Donnelly

Alex Donnelly

VP, Global Sales

Angie Piparo

Angie Piparo

VP, Global Operations

Design, automate, operate and publish data at scale
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