Data Analyst Lead/Architect

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 accumulation.

• Review and provide technical guidance on analytical solutions built by others, ensuring they conform to architectural standards and do not introduce technical debt or scalability risks.

Investigate operational signals and build analytical models

• Lead technical investigations into high-complexity, multi-system data problems — tracing issues across distributed telemetry, crash logs, device firmware signals, and cloud infrastructure metrics.

• Develop statistical and analytical models to detect anomalies, surface quality signals, and identify failure patterns — including time-series analysis, trend detection, cohort analysis, and threshold-based alerting.

• Apply machine learning techniques where appropriate — such as clustering for incident classification, regression for quality prediction, or NLP for support ticket analysis — using Python and relevant libraries.

• Write complex, optimised SQL, Python, and KQL scripts to extract, join, and model data from heterogeneous sources at scale, and build reusable investigation frameworks that accelerate future root cause analysis.

Deliver BI solutions and partner with Data Engineering

• Architect, build, and publish enterprise-grade Power BI solutions — including report design, semantic model and DAX optimisation, visual-level query reduction, deployment pipelines, and workspace governance.

• Define BI deployment standards: version control for Power BI assets, CI/CD integration, environment promotion (dev → test → prod), and dataset certification.

• Act as the analytical requirements owner toward Data Engineering — articulating precisely what data is needed, at what grain, freshness, and quality, and identifying when a new or improved data product is required.

• Review and validate data pipeline outputs, schema designs, and transformation logic to ensure they support downstream analytical use cases, and contribute to data contract and data quality framework definitions.

• Own analytical documentation — data dictionaries, model ERDs, measure glossaries, and lineage maps — ensuring the analytical layer is fully auditable.

Collaborate across functions

• Work closely with Incident Management, Support, SRE Engineers, Software Development, Data Engineering, and Legal stakeholders to understand their data needs and ensure analytical outputs are relevant and trustworthy.

• Act as the primary analytical point of contact — translating technical findings into clear, actionable insight for both engineering audiences and business stakeholders.

• Help ensure that data collection, usage, and retention practices are aligned with legal and compliance requirements, including GDPR and internal data governance policies.

What you bring

Experience

• 8+ years of experience in a data-focused technical role such as Senior Data Analyst, Analytics Engineer, BI Developer, or equivalent.

• BE/ B.Tech degree in Computer Science, Software Engineering, Data Science, or a related field; advanced degree preferred.

• Demonstrable experience designing and owning analytical data models in production — including star schema, semantic layers, and dimensional modelling patterns.

• Proven track record of delivering operational or product analytics in a software-driven environment, working across multiple data sources and stakeholder groups.

• Experience defining metrics frameworks, data contracts, and data quality rules that are adopted and trusted across the organisation.

• Demonstrated experience designing and owning analytical solution architecture — including layered consumption models, reusable semantic patterns, and technology decisions within the BI and analytical stack — with a focus on scalability and long-term maintainability.

Technical skills

• Expert-level proficiency in SQL — including advanced query optimisation, window functions, recursive CTEs, and execution plan analysis.

• Experience documenting and communicating architectural decisions (ADRs) — articulating design rationale, trade-offs, and implications clearly to both technical and non-technical audiences.

• Expert-level proficiency in Python for analytical work — including pandas, NumPy, scikit-learn, and visualisation libraries.

• Deep hands-on experience with Power BI — including DAX authoring, semantic model design, performance tuning, incremental refresh, and deployment pipeline management.

• Proficiency in KQL (Kusto Query Language) for querying operational and telemetry data in Azure Monitor, Log Analytics, and Application Insights.

• Experience working with Databricks or equivalent lakehouse platforms as an analytical consumer — querying Delta tables, working within Unity Catalog, and using notebooks for exploratory and production analytical work.

• Familiarity with Azure data services relevant to analytical consumption — including Azure Data Lake Storage, Azure Monitor, and Log Analytics.

• Understanding of distributed tracing standards such as OpenTelemetry and experience correlating trace data with analytical outcomes.

• Working knowledge of statistical modelling and applied machine learning techniques — anomaly detection, classification, clustering, and time-series forecasting — using Python.

• Deep understanding of data privacy, governance, and compliance requirements, including GDPR and data minimisation principles applied to analytical systems. .

Personal competencies

• Systems thinker with a strategic approach to platform design.

• Balances architectural excellence with practical delivery.

• Leads through technical expertise and hands-on collaboration.

• Communicates effectively with both technical and business stakeholders.

• Champions governance, quality, and continuous improvement.

Who we are

At WSA, we provide innovative hearing aids and hearing health services.

Together with our 12,000 colleagues in 130 countries, we invite you to help unlock human potential by bringing back hearing for millions of people around the world.

With us, you will become part of a truly global company where we care for one another, welcome diversity and celebrate our successes.

Sounds wonderful? We can't wait to hear from you.

WSA is an equal-opportunity employer and committed to creating an inclusive employee experience for all. Regardless of race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status we firmly believe that our work is at its best when everyone feels free to be their most authentic self.