Engineering Manager - Data & Ml Platform H/F

Contenu de l'offre Engineering Manager - Data & Ml Platform H/F chez PriceHubble

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Description
About PriceHubble

PriceHubble is a PropTech company with over 220 employees, set to radically improve the understanding and transparency of real estate markets based on data-supported insights. We aggregate and analyze a wide variety of large scale datasets, and apply state-of-the-art machine learning to generate high-quality valuations and predictive analytics for the real estate market. We are headquartered in Zürich, with offices in Berlin, Hamburg, Paris, Vienna, Prague, Amsterdam and Tokyo. We work on international markets and we are backed by world-class investors. We have a startup environment, low bureaucracy, and an international team and business.

The opportunity

You will BE part of the Data Products team, heading a team of experienced ML and software engineers who build PriceHubble's Data and ML Platform. The Data & ML Platform Manager has a key role in supporting the excellence of PriceHubble's products, by directing the development of the ideal environment for data engineering and science teams to innovate and deliver PriceHubble's world-class, high scale, customer-facing real-estate inference products.

As the leading member of the team, the empowerment and growth of your team members will BE your main responsibilities. Moreover, you value elegant and highly efficient engineering, and you derive great satisfaction from delivering very reliable and usable systems.

Through your contributions, you will help to :
- Build a team of experts focused on large scale data systems engineering.
- Manage, maintain and develop the organization's ML and Data platforms, with a focus on usability, reliability, performance and efficiency.
- Define robust, maintainable, state-of-the-art distributed system architectures, to identify improvements to engineering processes and to improve efficiency and reduce effort in platform operation by creating repeatable and automated processes.
- Steer the development of infrastructure and systems supporting the deployment, monitoring, optimization and scaling of AI-based prediction and insight services in production, from conception to launch. Define requirements, create a roadmap and work with engineers to build, release, assess and iterate.
- Optimize and streamline the work of data engineering and data science teams, by designing tools and products to facilitate data exploration, model training, deployment and serving processes to achieve insights quality and service quality SLOs.

We will also expect you to always stay at the forefront of ML engineering, quality management, cloud and data ops best practices in the industry.