Quality Engineer - Health Data

About the role

Manulife is looking for a Data Quality Engineer in Chengdu to ensure the quality, reliability and performance of health data products that support AI and analytics use cases for its Health business. The role contributes to regional health initiatives such as Provider Management Solutions, Health KPIs and claim driver analyses, and works with data scientists, AI teams and business leaders. It is a hybrid position.

Key responsibilities

• Support the QA strategy and execution plan so that health data on provider networks, claims efficiency and care quality is accurate, complete, consistent, timely and reliable, and provide insights for contract negotiation, network optimization, cost management and fraud detection.
• Work with data engineers, data analysts, product owners and business stakeholders to resolve quality issues, review code and deliverables, and keep quality standards and SDLC compliance up to date.
• Develop, run and maintain manual and automated tests for data pipelines, APIs, reports, dashboards and datasets, validate mappings, transformations and business rules, and build automated testing frameworks for CI/CD.
• Prepare test data, investigate defects and run integration, regression, end-to-end, performance and data quality tests.
• Define and report quality metrics, support quality audits and governance reviews, build quality dashboards and promote quality-by-design and automation.

Required profile

• Bachelor's or Master's degree, preferably in Statistics, Mathematics, Computer Science, Business Analysis or Data Analytics.
• 3-5 years of experience as a Data Quality Engineer, Data Analyst, Data Engineer, BI Analyst, BI Engineer or data-focused Business Analyst.
• Ability to explain technical concepts to non-technical users and work with diverse teams.
• Strong analytical, problem-solving and communication skills; project management and Agile experience are an advantage.

Required skills

• Data manipulation, warehousing, mining, extraction, visualization and ETL.
• Power BI, SQL, Python, R, Spark and Excel.
• Big data tools such as ADLS, Data Factory and Databricks.
• English written and spoken.