
Johnson & Johnson
Senior Manager E2E Test Strategy Lead (Enterprise Performance Management)
- Permanent
- Abu Dhabi, United Arab Emirates
- Experience 5 - 10 yrs
Job expiry date: 25/03/2026
Job overview
Date posted
08/02/2026
Location
Abu Dhabi, United Arab Emirates
Salary
AED 30,000 - 40,000 per month
Compensation
Comprehensive package
Job description
The Senior Manager E2E Test Strategy Lead is responsible for defining and executing the end-to-end test strategy and execution for Enterprise Performance Management (EPM) platforms within Johnson & Johnson’s global healthcare technology landscape. The role focuses on EPM implementations including Anaplan, SAP BW on HANA, SAP S/4HANA, SAP ECC, Reporting AFO, and SCA, ensuring quality, compliance, and business readiness across finance processes. Operating within the Technology Product & Platform Management function under QA/Test Engineering, the role aligns E2E testing strategy with EPM Business and Technical Product Owners, boundary product teams, transformation chapter leads such as Cutover Lead, Build Lead, and PMO, and other dependent transformation programs. The position incorporates AI-driven test design, risk-based regression insights, and approved GenAI tools to auto-draft test scenarios, perform AI-assisted impact analysis, accelerate test execution, summarize defects, cluster duplicates, and generate execution and executive reports, all in line with enterprise Responsible AI, AI governance, compliance frameworks, data sensitivity handling, and regulated environment requirements. The role ensures strong governance over all test cycles including FIT, BST, UAT, regression, and performance testing, accurate reporting through JIRA and X-Ray, adherence to industry best practices and quality standards, and continuous improvement through post-implementation reviews and lessons learned across EPM releases.
Required skills
Key responsibilities
- Design and define the end-to-end test strategy for Enterprise Performance Management implementations including Anaplan, SAP BW on HANA, Reporting AFO, and SCA
- Align E2E test strategy with EPM Business Product Owners, Technical Product Owners, boundary product teams, PMO, Cutover Leads, Build Leads, and other interdependent transformation programs
- Define, align, and execute test automation strategies to accelerate testing and deployment
- Incorporate AI-driven test design and risk-based regression insights using approved GenAI tools in compliance with Responsible AI and governance guidelines
- Establish governance for planning, preparation, coordination, and execution of all EPM test cycles including FIT, BST, UAT, regression, and performance testing
- Ensure accurate, compliant, and timely test planning and execution reporting using JIRA and X-Ray
- Lead development and operational execution of automated testing for Anaplan
- Communicate testing readiness, execution progress, cutover status, and go-live risks to stakeholders and leadership
- Leverage GenAI to draft test steps, summarize defects, cluster duplicates, and generate cycle-level execution and executive reports with human validation
- Ensure adherence to industry best practices, quality standards, and internal compliance requirements across all testing activities
- Conduct post-implementation reviews to identify lessons learned and continuous improvement opportunities
Experience & skills
- Demonstrate 7+ years of experience in test strategy and execution coordination at Manager or Senior Manager level for Anaplan, SAP S/4HANA, SAP ECC, or SAP BW on HANA implementations in global organizations
- Demonstrate 7+ years of experience coordinating testing for finance processes, preferably within Enterprise Performance Management environments
- Demonstrate at least 2+ years of experience leading and implementing test automation with a proven delivery track record
- Show proficiency in test planning, execution, and defect tracking tools including JIRA and X-Ray
- Demonstrate experience developing SDLC documentation and managing knowledge repositories
- Demonstrate familiarity with AI governance and compliance frameworks including model drift awareness, data sensitivity handling, and regulated or controlled environments following internal AI-ML and GenAI guidelines