Berlin-based data infrastructure startup Tower announced that it has raised $6.4 million (about €5.5 million) across pre-seed and seed funding rounds to build what it describes as the “data backbone” for the next generation of AI-assisted data engineering. The funding round included backing from DIG Ventures, Speedinvest, Flyer One Ventures, Roosh Ventures, Celero Ventures, and Angel Invest, along with angel investors including Jordan Tigani (CEO of MotherDuck), Olivier Pomel (CEO of Datadog), Ben Liebald (former VP of Engineering at Harvey.ai), and Maik Taro Wehmeyer (CEO of Taktile).
Tower was founded by Serhii Sokolenko (CEO) and Brad Heller (CTO), both former Snowflake engineers who set out to address what they see as a growing gap between the speed of modern data development and the complexity of the infrastructure required to run it in production. The company is focused on helping teams deploy and operate Python-based data pipelines and applications without needing to manage complex infrastructure.
The company’s platform combines a Python-native orchestrator, execution compute, and analytical storage designed to allow developers to move from building code to running production systems more quickly. Tower says this approach enables data engineers to write pipelines and applications using familiar Python tools and libraries and then deploy them directly into a managed environment that handles packaging, runtime operations, monitoring, and scaling.
Tower says the shift toward AI-assisted coding has accelerated the need for such infrastructure. While tools like AI coding assistants make it easier to build data applications quickly, the process of packaging, deploying, monitoring, and maintaining those applications in production remains complex. The company positions its platform as the solution to this “last mile” challenge for data engineering teams.
The platform is built around open technologies, including an Apache Iceberg-based analytical storage layer, enabling companies to maintain control over their data while still integrating with other data systems such as Snowflake or Databricks where needed.
According to the company, Tower currently supports batch jobs, short-running functions, and interactive applications such as notebooks, dashboards, and API endpoints. The new funding will help expand development of the platform’s storage and collaboration capabilities while growing the team and go-to-market operations.
Tower said the company currently has a team of 12 employees across several countries and is hiring engineers in Berlin and London as well as go-to-market staff in London and the United States.
KEY QUOTES:
“Brad Heller and I started Tower because we saw a major shift happening in data engineering. For years, the field had been shaped by overly complex big data platforms. But suddenly, a new generation of data engineers was emerging: building with open-source tools, writing data applications directly in Python, and moving much faster than the infrastructure around them was designed to support. The tooling had changed, but production infrastructure remained complex. There was still no great platform for data engineers who were no longer interested in a long-term relationship with Spark.”
“Tools like Claude made it dramatically easier for data engineers to build their own data applications. But they also made the missing piece much more obvious. Once a data engineer has Claude-coded their app, where does it actually run? How does it get packaged, deployed, instrumented, monitored, and improved when something breaks in production? The ‘last mile’ of AI-assisted data engineering still does not exist.”
“Tower gives builders the core primitives they need to go from idea to production: a Python-native orchestrator, Python execution compute, and analytical storage.”
Serhii Sokolenko, CEO of Tower
