OVERVIEW:

We are seeking a Senior QA Engineer to lead quality assurance efforts for data pipelines, ETL processes, reporting, and dashboards. As a key team member, you will drive automation initiatives, establish best practices, ensure data integrity, and collaborate across teams to deliver high-quality data solutions.

BASIC QUALIFICATIONS:

• Bachelor’s degree in Computer Science, Data Science, IT, or related field
• 5+ years of experience in data quality assurance, especially with data pipelines and analytics solutions
• Expertise in SQL and Python for data validation and automation
• Experience with QA tools like PyTest, Great Expectations, dbt tests
• Familiarity with BI tools such as Tableau or Power BI
• Strong understanding of data management principles (data lineage, metadata)
• Proven leadership, mentoring, and stakeholder management skills
• Experience working in Agile/Scrum environments
  Job Types: Full-time, Contract
  Pay: $75.00 - $80.00 per hour
  Expected hours: 40 per week

Benefits:

• 401(k)
• Dental insurance
• Health insurance
• Paid time off
• Vision insurance

Application Question(s):

• What is the most interesting/unique project you worked on and your role in it?
• What is your experience with Agile/Scrum teams?

Ability to Commute:

• Orlando, FL 32819 (Required)

Work Location: In perso

Partner with engineering, analytics, and product teams to develop test cases, translate requirements, and support data governance and UAT sessions.

• Data Governance & Quality Standards

• Quality Leadership & Strategy:

Lead QA efforts across data ingestion, transformation, and visualization. Define standards, mentor junior engineers, and promote a culture of quality.

• Testing Execution:

Design and execute test plans for data accuracy, completeness, and performance. Conduct data profiling and exploratory testing to identify systemic issues.

• Automation & Framework Development:

Build and maintain automated test suites supporting CI/CD pipelines and regression testing with tools like PyTest, Great Expectations, or dbt.

• Cross-Functional Collaboration:

Partner with engineering, analytics, and product teams to develop test cases, translate requirements, and support data governance and UAT sessions.

• Data Governance & Quality Standards:

Enforce best practices for data validation and contribute to enterprise data governance initiatives.

• Performance Monitoring & Issue Resolution:

Create dashboards and KPIs to track data quality. Troubleshoot performance issues and lead root cause analysis for complex data problems.

• Documentation & Reporting:

Maintain thorough test documentation, defect reports, and communicate findings effectively to technical and non-technical stakeholders.