Senior Analytics Engineer - Customer Operations

You independently own and deliver end-to-end data solutions - from integrating diverse data sources and modeling robust data objects, to conceptualizing and developing dashboards that enable data driven decisions across Customer Operations. You lead business-facing conversations with confidence - asking the right questions to uncover what stakeholders truly need, challenging assumptions where necessary, and turning requests into well-scoped, data-driven solutions. You act as a solution architect for complex analytics initiatives, turning business objectives into data solutions and owning the full cycle from discovery and requirements to implementation, quality, and stakeholder handover. You align closely with software development teams on data requirements, models, and interfaces - ensuring a seamless flow of information between analytics and engineering. You actively contribute to improving team processes and tooling, identifying opportunities for automation, efficiency, and scalability in analytics workflows.

Cooperative, innovative, piratesque, responsible, ambitious – these five values are what we practice and preach. We encourage everyone to think on their feet, and love it when they come up with bold or edgy ideas. Simplicity is at our core. We avoid the unnecessary and focus on what really matters – from overcoming challenges to celebrating our shared successes. With us, growth is a continuous journey. We also support you with EDU Points (CHF 2,000 for employees in Switzerland, EUR 1,600 for employees in Germany, and EUR 700 for our Serbian employees) for your professional development, so there’s no risk of you getting stuck in a rut.

• 3-5 years of experience in Data Engineering

• Completed degree (university / university of applied science / higher technical college)

• Several years of experience in analytics or data engineering, with strong SQL and Python skills; comfortable in environments where legacy and modern stacks coexist, taking a pragmatic approach to delivering value in both

• Deep expertise in sustainable data modeling and complex pipeline design, using orchestration tools like Dataform or dbt; solid understanding of modern data architecture concepts such as Data Mesh, applied in real production environments

• Strong analytical skills and proficiency in data visualization - whether building dashboards or conducting ad-hoc exploratory analysis; familiarity with statistical analysis and knowing when and how to apply the right tests is a plus

• Strong grasp of business KPIs and processes, with the communication skills to guide stakeholders through ambiguous problems and advocate clearly for tradeoffs between business value and technical effort

• Comfortable working in collaborative engineering workflows - Git, code review, CI/CD practices, and Scrum - treating data work with the same rigor as software development

• Hands-on experience setting up, running, and evaluating A/B tests and familiarity with how ML models are built and deployed in production are a plus

• Fluency in English, while German language is an advantage

• Using creativity and initiative to solve problems and achieve goals

• Think and act with an absolute respect of human dignity and being aware of the human consequences of your decisions