Senior Data & Analytics Engineer (Europe, 100% remote)

About the role

Join our fully remote, mission-driven team dedicated to pioneering sustainable inflight service management . Work on innovative technology solutions with global impact, collaborate within a diverse, international environment, and continuously enhance your skills through ongoing learning opportunities. Headquartered in Zurich, Switzerland with team members across 11 countries, we offer a dynamic and engaging company culture that celebrates creativity, collaboration, and inclusion. About the role We are hiring a Senior Data & Analytics Engineer to take end-to-end ownership of our data platform and BI layer . We are on a mission to contribute to a zero-waste future by drastically reducing food waste, fuel consumption, costs, and most importantly, CO₂ emissions in the airline industry. Every role in our company is a direct contributor to this vision - and as a Senior Data & Analytics Engineer you will be at the forefront of turning data into meaningful insights, empowering our customers to effectively use them and drive real impact. You will evolve a vendor-built BI solution into a modern, product-grade data platform and ensure that data is transformed into scalable, trusted, and actionable insights for both our customers and internal teams. You're not just building dashboards, you're enabling better decisions and measurable impact. This role combines: Data Engineering (pipelines, processing, customer facing exports, platform) Analytics Engineering (modeling, KPIs, semantic layer) Data Product Ownership (customer impact, insight generation) What You'll Own Platform, Architecture & Pipelines Own the end-to-end data platform ( Databricks and Microsoft Fabric ) Design and operate scalable pipelines and lakehouse architecture Build scalable ETL/ELT workflows and manage batch/near real-time processing Ensure data quality, reliability, observability, and performance Define standards, data contracts, and architecture principles (clean, modular, scalable data platform) Optimize cost (e.g. Databricks jobs) and workload distribution across platforms Data Modeling & Semantic Layer Own Power BI semantic models and KPIs Build clean, scalable dimensional models (star schema) Ensure performance and consistency of DAX, aggregations, and dataset refresh Customer-Facing Analytics Build and maintain customer-facing dashboards and embedded analytics Ensure consistent and trusted metrics across all outputs Enable self-service analytics with governance Maintain and monitor customer facing exports Data as Product Translate business and customer needs into scalable data solutions Define KPIs and reporting standards with Product Proactively build insights and identify opportunities to create additional customer value Drive measurable customer and business impact Platform Quality, Operations, Security & Governance Build necessary validation scripts, unit tests, data quality checks Implement CI/CD, testing, and versioning for pipelines and BI Support multi-tenant architecture, security, and governance Implement monitoring and alerting (pipelines, refreshes) Migration & Insourcing (Initial Focus) Take ownership of a vendor-built BI solution Reverse-engineer pipelines, logic, and reports Reduce technical debt and rebuild toward a clean architecture What We're Looking For Experience 5+ years in data engineering / analytics engineering for customer-facing BI products Experience in SaaS or product-driven environments Proven track record building scalable data platforms and customer-facing analytics Technical & Platform Skills Strong experience with: Databricks (Spark / PySpark, Delta Lake) Microsoft Fabric or Azure data stack Power BI (data modeling, DAX, performance) Advanced SQL and dimensional modeling (Kimball) expertise Lakehouse architecture & ETL/ELT design Multi-tenant data models & embedded analytics Engineering Practices CI/CD for data pipelines and BI assets Write reusable, modular, maintainable and testable code. Monitoring and data quality practices Version control (e.g., Git) Nice-to-have Azure infrastructure knowledge (IAM, networking) Data quality/testing frameworks (e.g., Great Expectations) Mindset Strong ownership and autonomy: You take full ownership of outcomes, not just tasks Impact-driven: You focus on real-world results and have strong intuition for identifying patterns, anomalies, and optimization opportunities Product thinker: You connect data to user value and turn raw data into clear, actionable insights Proactive: You proactively identify opportunities, validate ideas, and build reports, dashboards, and analyses without waiting for requirements. Domain expert: You understand business contexts quickly and translate problems into effect

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