Soda
Soda provides software that lets data teams define checks on their data, run automated tests, and alert on quality issues across warehouses, lakes, and pipelines. Its open-source CLI tool and SaaS platform serve data engineers and analysts who need visibility into whether their data is trustworthy.
The company raised EUR 11.5M in 2021, a further EUR 11.5M in 2022, and EUR 14M in 2024. It has publicly stated reaching financial self-sufficiency, a notable milestone in a period when many SaaS companies remained dependent on ongoing venture support.
Soda operates in the data observability space alongside companies like Monte Carlo and Great Expectations, and is relevant to any Belgian enterprise data stack conversation.
Funding History
- EUR 11.5M (2021)
- EUR 11.5M (2022)
- EUR 14M (2024)
Key People
Maarten Masschelein - co-founder, ceo
In the media3
In this episode introduction, Maarten Masschelein discusses his background in data, noting his early use of Excel and his passion for the impact data can make on decision-making. He identifies Christian and Benno Verhague as influential co-founders of Caliberm, expresses interest in data's application to fact-checking fake news, and indicates his goal for the podcast is to share new insights and ideas that listeners can apply to their daily work with data practitioners and technologists.
Maarten Masschelein, CEO and founder of Soda Data, discusses his 12-year career in data management working on the software side, including his own experience managing operational systems, data warehouses, and reporting responsibilities. He explains his passion for technology since childhood and his philosophy on changing perspectives in data work by understanding different stakeholders' roles and putting himself in their shoes.
Maarten Masschelein discusses how the forward-deployed engineer role has become critical at AI companies, describing it as a new bottleneck replacing traditional product and engineering bottlenecks. He illustrates this with an example from a customer engagement where a forward-deployed engineer quickly modified a machine learning matcher to handle edge cases in data deduplication, delivering results the next day and directly influencing the customer's decision to proceed.