Data Quality Assurance Lead

3 weeks ago


Charlotte, North Carolina, United States Forsyth Barnes Full time

Job Title: Data Quality Assurance Lead

Location: Remote

Rate: Up to $65 per hour

Sector: Retail

Employment Type: Contract-to-Hire

Forsyth Barnes is seeking a highly skilled Data Quality Assurance Lead to drive data quality initiatives and collaborate with cross-functional Agile teams. The ideal candidate will have a strong background in data engineering, analytics, and quality assurance.

This is a brand new role and the successful candidate 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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