Who are we?
Data experts on a mission to democratize data
Castor is the ultimate tool to maintain knowledge around data within a company. It automates, crowdsources, and prioritizes data documentation. Our product is collaborative and accessible to everyone, from domain experts to data professionals. By doing so, we enable every employee to find, understand, and trust data assets.
Created in 2020, by four data lovers who all experienced firsthand the pain of exploring data, Castor is already running in leading unicorns such as Canva, BackMarket, Gorgias, Stuart, ManoMano, Vestiaire Collective, or Sendinblue.
We have a bold vision to improve our product. To support it, we raised a $3m Seed round from Frst and accomplished Business Angels (founders of Dataiku, Zenly, Vestiaire Collective, etc).
OUR MISSION Build a world where everyone, regardless of their skill set, can find, trust and use data
Our Vision and Product
Bring visibility and trust in data to everyone
5 reasons why you should join us
You will work with talented coworkers who've built valuable enterprises and killer technical products before at Qonto, Payfit, Blablacar, Bannerman, Algolia
You will build a category-defining product. Notion redefined knowledge management. Slack redefined communication in teams. Castor redefines knowledge management for data.
You will have the flexibility to work hard and live your best life. Our team members hike, paint, surf, and raise children, but we also execute incredibly fast.
You will tackle interesting technical and go-to-market challenges that keep you sharp and on your toes.
We are backed by the best. Our investors have backed some of the best French companies Payfit, Owkin, Doctrine, Pigment
A world-class group of experts in their fields
Castor's founders felt the pain first-hand as data scientists or heads of data in companies like Qonto, Ubisoft and Payfit. Arnaud, CPO, developed the internal solution at Criteo. Amaury, CTO, scaled twice an engineering team at Withings and Qonto. Since then they've managed to recruit a stellar team of passionate engineers, designers, marketers.
Work hard, play hard
We value spending quality time outside of work as well. While the flexibility of remote work is awesome, we know that it could also get lonely and frustrating at times. This is why we like to make things a bit more interesting.
We are here on an adventure. We have fun along the way and spend quality time outside of work: lunchs, drinks, after-works, off-sites...
Castor Tech Team is based in Paris, Sales and Marketing are remote
We move fast. We are always looking for world-class profiles to join our team. We are sure you are one of them. So whatever is written in the job descriptions below, if you want to work with us, contact us! Here's my email: firstname.lastname@example.org
To go further
Here's some cool content to help you understand better what we do
Learn more about Castor with a quick demo of the tool.
How to build your data team? - Castor Blog
Peer reviewed by Kat Holmes - Data Director ITV As businesses recognize the decisive power of data to achieve business goals, most are hoping to put data in the driver's seat of their business and product strategies. This entails putting together a strong data team which can effectively propagate its insights across different areas of the business.
Data Catalog Benchmark for Mid-Market Companies - Castor Blog
In the past decades, organizations have come to realize the importance of leveraging data efficiently. We are witnessing a "data race", in which businesses seek to hire the best data talents. The result? businesses are now equipped with data engineers, data scientists, and data analysts, mastering cutting-edge tools to produce meaningful data analysis.
Guide to evaluating a data catalog - Castor Blog
Data catalogs were introduced to help data people find and understand data. Before data catalogs existed, data engineers, data analysts, and data scientists worked blind, deprived of visibility into data sets, their content, their quality or their usefulness. Consequently, they spent most their time trying to locate and understand data, often recreating data sets that already existed.