Job Opportunities API

Driven by the passion to improve quality of people’s lives, WSA continues to grow as market leader in the hearing aid industry. With our commitment to increase penetration in an underserved hearing care market, we want to accelerate our business transformation in order to reach more people, more effectively. We're looking for an experienced Lead Analyst lead/Architect to drive the technical vision, architecture, and delivery of our modern Azure-based data platform. This is a hands-on leadership role where you'll combine deep engineering expertise with architectural thinking to build scalable, governed, and AI-ready data solutions that power analytics and business decision-making across multiple mobile app brands. What you will do You will join our Software Excellence department and operate as the senior technical authority for operational analytics. You will define how data is modelled, how metrics are structured, how insights are architected — and you will translate complex technical patterns into actionable intelligence for the business. Model, architect, and govern analytical data Connect and integrate data from multiple sources — curated data products, product telemetry, logs, support data, incident records, and observability platforms — and define how each is structured, modelled, and surfaced for analysis and BI consumption. Architect dimensional and semantic models that underpin operational and product analytics, including star schema design, calculated measures, hierarchies, and row-level security in Power BI or equivalent. Apply appropriate modelling patterns — SCD, data vault, or medallion layer conventions — to the analytical use case, and resolve model performance bottlenecks such as query folding, aggregation tables, incremental refresh, cardinality management, and DAX measure optimisation. Own the canonical definition of operational and product quality metrics — specifying calculation logic, grain, refresh cadence, and interpretation guidance — and maintain a metrics catalogue that is discoverable and consistently applied across teams and tools. Establish the analytical contract between the data layer and the BI layer, ensuring metric logic lives in the right place in the stack and that naming conventions and model standards are reused rather than duplicated. Drive analytical solution architecture Own the analytical architecture end-to-end within the consumption layer — defining how data moves from source systems through transformation, modelling, and semantic layers into BI and reporting surfaces, and ensuring each layer has clear responsibilities and boundaries. Design for scalability — architect semantic models, datasets, and analytical pipelines that remain performant and manageable as data volumes grow, user concurrency increases, and the number of reports and consumers expands. Design for maintainability — apply modular, layered design principles that separate concerns: raw data prep, business logic, metric definitions, and presentation. Ensure that changes in one layer do not cascade unpredictably into others. Establish reusable architectural patterns and standards for the analytical layer — including dataset certification tiers, shared dimension models, centralised metric tables, and report template structures — so that future analytical work builds on a consistent foundation rather than starting from scratch. Evaluate and govern technology choices within the analytical stack — making deliberate decisions about when to use DirectQuery vs. import, composite models vs. aggregations, Power BI dataflows vs. upstream transformations — with explicit reasoning around performance, freshness, and maintainability trade-offs. Document architectural decisions (ADRs) for significant design choices — capturing context, options considered, decision rationale, and implications — so the analytical architecture evolves deliberately rather than by