Quality Assurance Automation Lead

Groupe Kardol; Manhattan Beach (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Groupe Kardol seeks an experienced QA Lead to embed AI-native, automation-first quality engineering within the BI development lifecycle. You will champion data and BI testing across dashboards, ETL processes, and pipelines, driving autonomous defect management and objective quality visibility.

The role emphasizes reducing cycle times, fewer post-release defects, and building scalable automation assets. Collaboration with stakeholders is essential to align QA with business goals and BI outcomes.

Qualifications

• Bachelor’s degree in computer science, Information Technology or related field.
• Minimum of 10+ years of experience in a Testing/QA role, with a focus on BI and ETL testing.
• Strong SQL and Python skills.
• Experience validating dashboards, reports, KPIs, and underlying data across BI pipelines.

Responsibilities

• Embed an AI-native QE capability within BI delivery and lead automation-first testing.
• Design and implement automated test strategies across functional, non-functional, regression, and data validation scope.
• Develop reusable QA assets, automation frameworks, and governance processes for long-term efficiency.
• Provide scorecards on coverage, cycle time, and defect leakage.

Skills

Automation testing

BI testing

Data validation

SQL

Python

QA leadership

Agile

Communication

Education

Bachelor’s degree in CS/IT

Tools

Jira

Incorta

ETL tools

Job description

Purpose of the Position

The purpose of this position is to embed an AI-Native, automation-first Quality Engineering capability within the BI development lifecycle, ensuring faster, more reliable, and business-ready releases. The QA Lead will champion agentic automation across functional and non-functional data and BI validation, regression engineering, data reconciliation, and autonomous defect management. By integrating directly into BI delivery, this role eliminates traditional handoffs, accelerates release cycles, and provides objective quality visibility through measurable scorecards. The QA Lead will also oversee data validation across pipelines, lightweight engineering quality checks, and observability enablement to guarantee accuracy, integrity, and performance of BI assets. Through close collaboration with development and business stakeholders, the QA Lead will transform QA from a manual, reactive function into a proactive, AI-driven quality engineering practice—delivering reduced cycle times, fewer post-release defects, and sustainable automation assets that enhance BI ecosystem and overall organizational success.

Key Result Areas and Activities

1) Automation-First Test Planning and Execution: Design and implement automated test strategies across functional, non-functional, regression, and data validation scope. Leverage AI agents for automated test case generation, edge case identification, and autonomous defect validation.

2) Process Transformation and Continuous Improvement: Lead the transition from manual QA to embedded QE POD. Establish baseline metrics (cycle time, defect leakage, automation coverage) and drive measurable improvements. Develop reusable QA assets, automation frameworks, and governance processes to sustain long-term efficiency.

3) AI-Native QE Leadership: Lead embedded QE POD within BI delivery. Champion automation-first, agentic testing practices.

4) Quality Governance: Provide scorecards on coverage, cycle time, and defect leakage. Maintain regular quality operating rhythm with stakeholders.

Essential Skills

• Design and implement automated test strategies across functional, non-functional, regression, and data validation scope.
• Proven experience in leveraging AI agents for automated test case generation, edge case identification, and autonomous defect validation.
• Strong automation mindset for BI and data testing, minimizing manual QA.
• Extensive experience in end-to-end validation of dashboards, reports, KPIs, and underlying data.
• Proven experience in end-to-end testing which includes ETL and BI testing
• Proficiency in SQL and python
• Good experience in designing profiling rules and reconciliations to ensure data accuracy across pipelines.
• Hands-on expertise in building regression packs and automated test scripts.
• Ability to create detailed and comprehensive test cases for data, functional, and performance testing.
• Excellent teamwork and communication skills for effective collaboration with business teams.

Desirable Skills

• Experience in Agile frameworks (Scrum, Kanban) and experience with tools like Jira.
• Familiarity with additional BI tools beyond Strategy such as Incorta.
• Understanding of data warehousing principles and architecture.
• Nice to have basic knowledge of cloud services

Qualifications

• Bachelor’s degree in computer science, Information Technology or related field.
• Minimum of 10+ years of experience in a Testing/QA role, with a focus on BI and ETL testing.