Staff Engineer, Data & Analytics

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 550+ employees supports 1,500+ customers - reaching over 14 million employees - in transforming their employee experience.
We are proud to be aUnicorncompany - 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