Hybrid Remote (3 Days in Office in Charlotte, North Carolina)
Contract to Hire
Must be eligible to work in the U.S. without sponsorship.
Stack Ranked Experience (Must Have):
ETL Testing
Azure Data Analytics (Data Warehouse, Power BI
Azure Data Factory, Databricks, Python
Agile Methodology
Managerial Experience of a Small QA Team
Position Summary:
The Data Quality Assurance Lead plays a critical role in improving data quality across the organization by working closely with data engineering, analytics, and other Agile teams. This individual will support the development and migration of data into a modern, cloud-based data infrastructure and contribute to multi-year digital transformation initiatives. The ideal candidate will lead the QA efforts for data engineering and BI projects, driving best practices and ensuring high standards in data quality and integrity.
Key Responsibilities:
Collaborate with key stakeholders and the data engineering team to define data requirements, review data workflows, and ensure the delivery of accurate, high-quality data products.
Develop innovative solutions for measuring and improving data quality and performance, challenging existing processes and implementing new standards.
Create, design, and execute comprehensive QA strategies for data engineering and BI products, focusing on functional, integration, regression, and performance testing.
Act as the primary point of contact for data quality issues across core data domains, ensuring data integrity and consistency throughout the data lifecycle.
Develop and monitor key performance indicators (KPIs) related to data processes and data quality for the organization's global data platform.
Identify opportunities for process improvement and automation within the QA and data engineering processes, leveraging tools like Databricks and Python to streamline workflows.
Serve as a liaison between QA, development, and business teams, ensuring alignment on project goals and quality expectations.
Monitor process performance, provide regular reports, and suggest data quality improvements.
Qualifications:
Bachelor's degree in Computer Science, Information Systems, or a related field.
12+ years of experience in QA/testing with a focus on data-centric projects, including at least 2 years in a lead or managerial role.
Extensive hands-on experience in ETL/ELT testing, data quality reporting, and performance tuning using tools like SSIS or Azure Data Factory in a Data Warehousing environment (e.g., SQL DB/Synapse).
Proficiency in Azure platforms, including Data Factory, Synapse Analytics, Databricks, Blob Storage, and Power BI, as well as strong Python scripting skills.
Experience defining and enabling data quality standards for auditing and monitoring purposes.
Expertise in data engineering concepts, including data modeling, ETL processes, and database management.
Strong proficiency in SQL for data querying, test validation, and analysis.
Demonstrated experience managing large-scale data environments and bringing rigor and structure to unstructured data processes.
Preferred Qualifications:
Experience in the retail industry or related sectors, with knowledge of retail operations and data challenges.
Certifications in Quality Assurance, Data Engineering, or cloud platforms (e.g., Azure).
Familiarity with cloud-based data services and architectures, including continuous integration, regression testing, and data versioning best practices.