About the role
About Staffbase We inspire people to achieve great things together. Our mission is to help organizations unlock the power of inspirational communication with the first AI-native Employee Experience Platform . Our industry-leading and award-winning agentic AI communications channels - intranet, employee app and email solutions - create engaging experiences that connect and empower employees. Headquartered in Chemnitz, Germany and New York City, with offices in Berlin, London, Sydney, Tokyo, Prague, and Minneapolis–St. Paul, our diverse team of 750+ employees supports 2,000+ customers—reaching over 16.4 million employees—in transforming their employee experience. We are proud to be a Unicorn company—privately valued at over $1 billion—demonstrating strong growth, innovation, and lasting impact in our industry. Together, we’re shaping the future of workplace communication. As a Staff Engineer in our Data & Analytics team, you will drive the technical direction of our data platform and analytics capabilities. This is a hybrid role combining hands-on technical leadership with people leadership. You will work closely with the Director of Engineering and cross-functional stakeholders to advance our data infrastructure and lay the foundations for AI-powered product features. Our environment Work alongside skilled, data-passionate engineers in a team that values collaboration A flexible, agile environment with a strong focus on work-life balance You will have real ownership and influence over data architecture We are building toward a distributed, self-service data platform model – you will help shape how that evolves. Data is a strategic asset at Staffbase: valued, actively growing, and directly enabling our AI product roadmap. What you'll be doing Drive the technical direction of our data platform – leading architecture decisions, sequencing, and delivery in collaboration with Engineering, Product, and Design stakeholders Support and develop a team of 4–5 engineers: providing technical mentorship, unblocking delivery, and helping build a product-minded engineering culture. Own and evolve the data infrastructure: data lake, data foundations, semantic layer, and data governance – building a solid base for AI and analytics features Lead streaming adoption across