Job Description

Accountabilities:

• Take end-to-end ownership of the data platform, including Databricks, Microsoft Fabric, scalable pipelines, and lakehouse architecture.
• Design, build, and operate robust ETL/ELT workflows supporting batch and near-real-time data processing.
• Establish and maintain data quality, reliability, observability, performance, and architectural standards across the platform.
• Define data contracts, architecture principles, and modular, scalable approaches while optimising platform costs and workload distribution.
• Own Power BI semantic models, KPIs, dimensional models, DAX performance, aggregations, and dataset refresh processes.
• Build and maintain customer-facing dashboards, embedded analytics, and data exports while ensuring consistent and trusted metrics.
• Enable governed self-service analytics and support secure, scalable multi-tenant data models.
• Translate customer and business needs into scalable data products, define reporting standards with Product, and identify opportunities to create additional value through data.
• Implement testing, validation, monitoring, alerting, CI/CD, versioning, and other engineering practices across data pipelines and BI assets.
• Lead the transition from an existing vendor-built BI solution by reverse-engineering pipelines, logic, and reports, reducing technical debt, and rebuilding toward a clean, product-grade architecture.
• Proactively identify patterns, anomalies, optimisation opportunities, and new insights that can generate measurable customer and business impact.

Requirements:

• 5+ years of experience in data engineering or analytics engineering, ideally focused on customer-facing BI products.
• Proven experience building scalable data platforms and customer-facing analytics in SaaS or product-driven environments.
• Strong hands-on expertise with Databricks, including Spark/PySpark and Delta Lake.
• Strong experience with Microsoft Fabric and/or the broader Azure data ecosystem.
• Advanced Power BI expertise, including data modelling, DAX, performance optimisation, and semantic models.
• Advanced SQL skills and strong knowledge of dimensional modelling, particularly Kimball methodology.
• Strong understanding of lakehouse architecture, ETL/ELT design, multi-tenant data models, and embedded analytics.
• Experience implementing CI/CD for data pipelines and BI assets, as well as version control using tools such as Git.
• Strong engineering discipline, including writing reusable, modular, maintainable, and testable code.
• Experience with data quality, monitoring, validation, and testing practices.
• Knowledge of Azure infrastructure, including IAM and networking, is a plus.
• Familiarity with data quality frameworks such as Great Expectations is an advantage.
• Strong ownership and autonomy, with a focus on outcomes rather than simply completing assigned tasks.
• Product-oriented and impact-driven mindset, with the ability to connect technical data solutions to customer value.
• Proactive and pragmatic approach, with a preference for simple, effective solutions over unnecessary complexity.
• Strong communication skills and the ability to make complex technical concepts accessible to both technical and business stakeholders.
• Minimum English proficiency of C1.
• Willingness to participate in two annual one-week team events.

Benefits:

• Fully remote working environment within Europe.
• Opportunity to take significant ownership of a modern data platform and BI ecosystem.
• Broad scope combining data engineering, analytics engineering, and data product ownership.
• Opportunity to work on technology designed to reduce food waste, fuel consumption, costs, and CO₂ emissions in the aviation industry.
• International and diverse working environment with colleagues across multiple countries.
• Strong emphasis on continuous learning, collaboration, creativity, and inclusion.
• Opportunity to build customer-facing analytics and data products with measurable real-world impact.
• Two annual one-week team events offering opportunities for in-person collaboration.
• High level of autonomy and flexibility in how you approach technical challenges and deliver outcomes.