Position Title: Enterprise QA Architect (Data / AI / Apps)
Location: Remote
Duration: 3 Months contract
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Position Overview
Our client is building an enterprise-scale AI Health Cloud ecosystem on Microsoft Azure and Databricks platforms. The initiative includes multiple Data and AI-driven software products that require a robust, scalable, secure, and automation-first Enterprise Testing Strategy.
We are seeking an experienced and hands-on Enterprise QA Architect who will take end-to-end ownership of defining, enhancing, governing, and implementing the Enterprise Testing Strategy across all client products and workstreams.
The ideal candidate will possess strong expertise in enterprise quality engineering, test automation, performance engineering, API testing, security testing, data validation, cloud-native platforms, and healthcare-compliant software delivery practices. This role requires both strategic leadership and hands-on technical execution capabilities.
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Key Responsibilities
Enterprise QA Strategy & Architecture
- Own and drive the End-to-End Enterprise Testing Strategy across all products.
- Define enterprise-wide QA standards, frameworks, governance models, and testing best practices.
- Establish scalable Quality Engineering processes aligned with Agile, DevOps, and CI/CD methodologies.
- Design enterprise testing architecture covering:
- Functional Testing
- Non-Functional Testing
- Security Testing
- Data Validation Testing
- API Testing
- Integration Testing
- Performance Engineering
- Regression Testing
- User Acceptance Testing
- AI Testing
- DR Testing
- Define quality gates, release readiness criteria, and test governance processes.
- Establish enterprise-wide test metrics, KPIs, dashboards, and reporting mechanisms.
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Test Automation Leadership
- Lead the implementation of an Automation-First Testing Strategy targeting high automation coverage across products.
- Architect scalable automation frameworks for:
- UI Automation
- API Automation
- Data Validation & Reconciliation
- Regression Testing
- Integration Testing
- Standardize reusable automation components, libraries, accelerators, and utilities.
- Drive shift-left testing practices across development teams.
- Collaborate with DevOps Engineers for integration of automated testing into Azure DevOps CI/CD pipelines.
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Test Framework & Tooling Ownership
- Define and govern the enterprise QA tooling ecosystem.
- Implement and manage testing tools including:
- Pytest
- Playwright
- Postman
- JMeter
- Grafana k6
- Azure Test Plans
- Azure Boards
- Evaluate and recommend additional testing and observability tools where required.
- Establish enterprise test repositories, versioning standards, and framework governance.
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Data & AI Platform Testing
- Define testing strategies for:
- Azure-based cloud applications
- Databricks workloads
- Data Engineering pipelines
- Data Warehouse migration initiatives
- Semantic Layer implementations
- Power BI reporting ecosystems
- AI/ML-enabled platforms
- Lead enterprise data quality validation and reconciliation strategies.
- Define validation frameworks for:
- ETL/ELT Pipelines
- Data Integrity
- Data Consistency
- Data Reconciliation
- Business Rule Validation
- Collaborate with Data Engineering and AI teams to validate model outputs and data processing workflows.
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Security & Compliance Testing
- Collaborate with Security Team for Security Testing using enterprise security testing strategy including:
- SAST
- DAST
- SCA
- Penetration Testing
- Ensure testing processes comply with healthcare and PHI data security standards.
- Establish secure test data management strategies across environments.
- Define governance for synthetic, non-production, and production data usage.
- Collaborate with security and compliance teams to address vulnerabilities and audit requirements.
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Performance Engineering
- Architect enterprise performance testing strategy covering:
- Load Testing
- Stress Testing
- Volume Testing
- Scalability Testing
- Define performance benchmarks, SLAs, and monitoring frameworks.
- Identify system bottlenecks and optimization opportunities across cloud-native platforms.
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Environment & Release Management
- Define and govern multi-tier testing environments:
- DEV
- INT
- QA
- PROD Parallel
- PROD
- Establish environment readiness and deployment validation processes.
- Collaborate with DevOps and Infrastructure teams to ensure stable and scalable testing environments.
- Drive release quality governance and production readiness assessments.
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Stakeholder Collaboration & Leadership
- Work closely with:
- Product Owners
- Engineering Teams
- Solution Architects
- Data Engineers
- AI/ML Teams
- Security Teams
- Business Stakeholders
- Client Leadership
- Lead QA governance meetings, defect triage sessions, and release reviews.
- Mentor QA engineers and establish enterprise QA capability maturity.
- Provide strategic recommendations to improve software quality, automation maturity, and delivery efficiency.
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Required Skills & Experience
Technical Skills
Mandatory
- Strong experience in Enterprise QA Architecture and Quality Engineering.
- Hands-on expertise with:
- Playwright
- Pytest
- Postman
- JMeter / Grafana k6
- Azure DevOps
- Azure Test Plans
- Azure Boards
- Strong understanding of:
- Test Automation Frameworks
- API Testing
- Integration Testing
- Security Testing
- Performance Testing
- Experience testing:
- Cloud-native applications
- Azure services
- Databricks platforms
- Data Engineering pipelines
- Power BI ecosystems
- Strong SQL and data validation expertise.
- Experience with enterprise test data management and reconciliation frameworks.
- Experience with Agile/Scrum delivery models.
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Preferred Skills
- Experience in Healthcare, PHI, HIPAA, or regulated industry environments.
- Exposure to AI/ML system validation and testing.
- Experience with large-scale enterprise transformation programs.
- Familiarity with observability and monitoring platforms.
- Experience building enterprise reusable QA accelerators/frameworks.
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Experience Requirements
- 10+ years of experience in Software Testing / Quality Engineering.
- 5+ years of experience in QA Architecture or QA Leadership roles.
- Proven experience implementing enterprise-scale automation and testing strategies.
- Experience leading QA for Data, Cloud, and AI transformation initiatives.
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Educational Qualifications
- Bachelor’s or Master’s degree in:
- Computer Science
- Information Technology
- Software Engineering
- Data Engineering
- Related discipline
Preferred Certifications
- ISTQB Advanced Level
- Azure Certifications
- Certified Scrum Master (CSM)
- Performance Testing / Security Testing Certifications
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Key Success Metrics
- Increased automation coverage across enterprise testing landscape.
- Reduced defect leakage into production.
- Improved release quality and deployment confidence.
- Faster testing cycles and reduced regression effort.
- Improved security and compliance posture.
- Standardized QA governance across all AI Health Cloud products.
- Enhanced traceability, reporting, and audit readiness.
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Ideal Candidate Profile
The ideal candidate is a strategic QA leader with strong hands-on engineering capabilities who can establish enterprise-grade testing practices for complex Data and AI ecosystems. The individual should be capable of driving both architectural decisions and technical implementation while collaborating effectively with cross-functional global teams.