Data validation: Verify data accuracy and completeness by comparing source data with transformed data in Azure Synapse. Develop and implement tests to ensure data quality meets defined standards. Monitor data pipelines for errors and data discrepancies.
Data profiling: Analyze data distributions, identify anomalies, and ensure data consistency across datasets. Report data quality issues to data engineers and analysts for resolution.
Metadata validation: Verify that data dictionaries and other metadata accurately reflect the actual data content. Ensure consistent terminology and labeling across data assets.
Power BI Testing and Validation
Report and dashboard testing: Test Power BI reports and dashboards for visual accuracy, data correctness, and functionality. Ensure consistency between reported data and underlying datasets. Identify and report bugs or usability issues in the dashboards.
Performance testing: Evaluate the performance of Power BI reports and dashboards under different data loads and user actions. Recommend optimizations for faster report loading and user interactions.
Accessibility testing: Ensure Power BI reports and dashboards are accessible to users with disabilities. Follow accessibility guidelines and best practices.
General Quality Assurance
Documentation review: Review technical documentation for accuracy, completeness, and clarity. Ensure documentation reflects current data pipelines, Power BI reports, and processes.
Process improvement: Identify opportunities to improve data quality, testing procedures, and documentation. Suggest process automation and optimization techniques.
Collaboration: Work closely with data engineers, analysts, and developers to resolve data quality issues, address bugs, and improve overall analytics platform quality.
Compliance testing: Ensure data handling and reporting practices comply with relevant regulations and data privacy laws. Conduct security audits and vulnerability assessments for the analytics platform.
Knowledge sharing: Create training materials and knowledge base articles on data quality practices and Power BI testing. Share expertise with other team members to promote a data-quality culture.
All other duties and responsibilities as assigned
Skills Needed To Be Successful
Understanding of laboratory workflows and processes
Familiarity with relevant laboratory procedures and tests
Awareness of regulatory requirements and compliance standards
Excellent attention to detail and follow-up.
Ability to think creatively, identify alternatives, and generate new ideas necessary to achieve a goal
Ability to represent software workflows, architecture, entities and relationships via different visual and/or language methods
Ability to perform root cause analysis to identify the most important contributing factor(s) to an issue
Required Experience & Education
Bachelor's degree in software engineering, mathematics, or related discipline, or equivalent relevant work experience
At least 2 years of experience with the following or similar:
Data Quality and Validation: Azure Synapse Analytics Workspace, Azure Data Factory, Azure Data Explorer, Azure Data Catalog, Power BI Desktop
Power BI Testing and Validation: Power BI Desktop and Service, Azure Monitor, Azure Application Insights, Power BI Desktop Performance Analyzer
General Quality Assurance: Azure DevOps, Azure Pipelines, GitHub or Azure Repos, Azure Key Vault
Additional Services: Azure Security Center, Azure Sentinel, Azure Data Share
Preferred Experience & Education
Knowledge of scientific data software, medical devices, or healthcare software
Knowledge of Cloud based application testing
Scripting or programming languages like .Net or C# or Python might be used for automated data validation or Power BI testing.
