Quality Assurance Manager
1 month ago
Job Title: Data Quality Assurance Lead
Location: Charlotte, NC (3 days in-office)
Rate: Up to $65 per hour
Sector: Retail
Employment Type: Contract-to-Hire
The Data Quality Assurance Lead will drive data quality initiatives, collaborating with data engineering, analytics, and cross-functional Agile teams. The role includes supporting data lineage, defining critical data elements, and managing data migration to a modern cloud infrastructure. This position will contribute to long-term initiatives focused on digital transformation.
This is a brand new role and you will be given the opportunity to grow a team up from scratch to approximately 4-6 staff. The role will be 75% hands-on and 25% team management.
Key Responsibilities:
- Collaborate with stakeholders and data engineering to define data requirements, understand workflows, and review the end-to-end data journey.
- Innovate and implement new data quality and performance metrics, challenging existing processes.
- Develop QA strategies for data engineering and BI products, ensuring data accuracy, completeness, and reliability.
- Lead functional, integration, regression, and performance testing to ensure data quality.
- Serve as the main contact for data quality within relevant data domains, and contribute to standardizing data requirements across workflows.
- Design and monitor key performance indicators (KPIs) related to data processes and quality.
- Identify process improvements and implement automation tools to enhance efficiency.
- Liaise between QA, development, and business teams to ensure alignment on project goals and quality expectations.
- Monitor and improve process performance, suggesting data quality enhancements.
Required Experience:
- Bachelor’s degree in Computer Science, Information Systems, or a related field.
- 12+ years of QA/testing experience, with 2+ years in a lead role on data-centric projects.
- Strong hands-on experience with ETL/ELT testing, data quality reporting, and performance tuning, especially with SSIS or Azure Data Factory in a multidimensional data warehousing environment.
- Expertise with Azure-based solutions, including Synapse, Databricks, Power BI, SQL, and Python/PySpark.
- Proficiency in defining data quality standards and monitoring data processes.
- Strong knowledge of data engineering concepts, including ETL, data modeling, and database management.
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