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 a Senior Data Engineer to design, build, and optimise modern data pipelines on our Azure data platform. You'll play a key role in delivering high-quality, production-ready data products, working closely with architects, analysts, and fellow engineers to create scalable, governed, and reliable data solutions. What you will do You will join the Data Platform & Products Team as the primary engineering delivery lead, building and operating the data pipelines and processing frameworks that form the backbone of our Lakehouse platform. Build and maintain scalable data pipelines Design, develop, and maintain scalable data pipelines that ingest, transform, and deliver data across Bronze, Silver, and Gold layers. Build ETL/ELT processes using Databricks and Apache Spark, integrating data from multiple enterprise systems and source platforms. Implement data cleansing, validation, enrichment, and aggregation processes that ensure data is fit for analytical and compliance use cases. Process structured and semi-structured data at scale, optimising for both performance and cost efficiency. Ensure data quality and observability Implement automated data quality checks and validation controls at each layer of the pipeline. Establish monitoring and alerting for data pipelines — covering data freshness, completeness, schema drift, and processing failures. Investigate and resolve data quality incidents, and build the observability tooling that prevents them from recurring. Contribute to the definition and enforcement of data quality standards in collaboration with the Data Architect and Systems Analyst. Operate and optimise the platform Support deployment automation and CI/CD practices — including pipeline versioning, environment promotion, and automated testing. Optimise platform performance and operational costs — identifying inefficiencies in Spark jobs, cluster configurations, and storage patterns. Maintain platform reliability and availability, and participate in on-call or support rotations for production data pipeline issues. Collaborate across the team Work closely with the Data Architect on solution implementation — translating architectural designs into reliable, production-grade code. Partner with the Systems Analyst to understand business requirements and ensure pipeline outputs meet the expectations of data consumers. Support delivery of certified Gold layer data products that analysts and downstream consumers can rely on. What you bring 5 to 8 years of experience in Data Engineering or a closely related engineering role. BE/ B.Tech degree in Computer Science, Software Engineering, Data Science, or a related field; advanced degree preferred. Experience building and maintaining enterprise-scale data pipelines in a cloud environment. Experience working with production data platforms — including incident management, performance tuning, and operational support. Experience with CI/CD practices applied to data engineering workflows. Technical skills Azure Databricks and Apache Spark (PySpark) — notebook-based development (.ipynb), job authoring, and cluster management Python and SQL at a strong proficiency level Delta Lake and Medallion Architecture (Bronze, Silver, Gold) — including UID-based MERGE, partition strategies, and write mode selection Azure Data Lake Storage Gen2 and Unity Catalog — volume management, schema organisation, and Delta table governance Databricks Workflows — multi-task job definition, dependency management, and scheduling Databricks Asset Bundles (DAB) — bundle configurati