Senior Business Analyst - Operations IT

AstraZeneca•Frederick, MD

6d•Hybrid

About The Position

The Senior Business Analyst will partner with manufacturing, quality, supply chain, engineering, facilities, technical operations, and IT teams at AstraZeneca’s Frederick Manufacturing Center to address operational challenges through robust, scalable technology. This hands-on role shapes technology solutions with technical teams and suppliers, investigates and resolves system issues, supports testing and deployment, and helps ensure technology delivers measurable value in a regulated, safety-first environment. The role is hybrid in name but requires an average of at least four days per week on-site; remote working is not available. Occasional domestic and international travel may be required. The role translates stakeholder priorities and business needs into technology roadmaps and well-defined requirements. It supports delivery across the full lifecycle, from process design and solution assessment through test planning, user acceptance testing, defect triage, deployment readiness, and hypercare. The Senior Business Analyst develops a detailed understanding of systems, integrations, data flows, and operational controls; investigates incidents and root causes; and ensures delivery considers GxP, data integrity, cybersecurity, validation, and quality expectations. The role also measures business outcomes and technology performance, captures lessons learned, and improves solutions to drive adoption and long-term value. The position calls for a bachelor’s degree and at least 10 years of relevant experience in business analysis, IT delivery, digital transformation, or manufacturing technology in a complex operational environment. The successful candidate will have strong business analysis and delivery skills, be comfortable working across integrated systems and regulated environments, and collaborate effectively with global stakeholders, technical teams, and suppliers. Familiarity with regulated manufacturing, biologics operations, manufacturing platforms, analytics, continuous improvement, and AI-enabled solutions is desirable.

Requirements

Bachelor’s degree.
At least 10 years of experience in business analysis, IT delivery, digital transformation, or manufacturing technology within a complex operational environment.
Strong expertise in requirements elicitation, process mapping, stakeholder management, problem-solving, prioritisation, and workshop facilitation.
Experience working with complex, integrated IT systems and strong understanding of how systems, data, integrations, and business processes interact to support operations.
Experience collaborating directly with architects, developers, engineers, data specialists, IT support teams, and technology suppliers to shape solutions, resolve issues, and deliver practical outcomes.
Experience supporting technology delivery using Agile, Waterfall, or hybrid methodologies, including requirements refinement, testing support, UAT, defect management, and deployment readiness.
Experience in regulated industries and knowledge of quality, compliance, GxP, data integrity, cybersecurity, and validation or assurance requirements.
Excellent communication, influencing, and relationship-management skills, with experience engaging business users, technical specialists, senior stakeholders, and external vendors or system integrators.
Proven ability to work effectively in a global, matrixed, and fast-paced environment.
Understanding of how data, automation, analytics, and artificial intelligence, including generative AI, can support business and manufacturing processes; ability to identify use cases, define requirements, assess risks, and measure value.
Ability to work on-site an average of at least four days per week at the Frederick Manufacturing Center.
Willingness to undertake occasional domestic and international travel, as required.

Nice To Haves

Experience in pharmaceutical, biotechnology, medical device, or other regulated manufacturing industries.
Understanding of biologics manufacturing, quality, laboratory, engineering, warehouse, supply chain, or technical operations processes.
Experience with Manufacturing Execution Systems (MES), Electronic Batch Records (EBR), Laboratory Information Management Systems (LIMS), Enterprise Resource Planning (ERP), digital quality management systems, CMMS/EAM platforms, manufacturing data historians, or analytics platforms.
Knowledge of system integrations, APIs, data flows, reporting, dashboards, and operational analytics.
Lean, Six Sigma, process excellence, or continuous-improvement experience.
Formal business analysis certification such as IIBA, BCS, or PMI-PBA.
Experience supporting the design, delivery, adoption, or scaling of AI, generative AI, advanced analytics, intelligent automation, or digital assistant capabilities.
Understanding of responsible AI principles, including data quality, privacy, cybersecurity, human oversight, validation, and change management within regulated environments.

Responsibilities

Engage site and global stakeholders to understand priorities, challenges, and business needs.
Translate business requirements into an outcomes-focused technology roadmap to improve safety, reliability, and efficiency across biologics manufacturing operations.
Lead workshops and design sessions to document current-state and future-state processes.
Produce requirements, user stories, acceptance criteria, and delivery artefacts.
Partner with Product Owners, Solution Architects, IT Support teams, and external suppliers to evaluate options and recommend scalable, practical, supportable solutions aligned with enterprise standards.
Develop a detailed understanding of systems, integrations, data flows, and operational controls.
Investigate incidents, perform root-cause analysis, and drive timely resolution to protect manufacturing operations.
Contribute to solution design, test planning, user acceptance testing, defect triage, deployment readiness, and hypercare support.
Ensure delivery considers GxP requirements, data integrity, cybersecurity, validation, and quality expectations.
Measure business outcomes and technology performance, capture lessons learned, and continuously improve solutions to drive adoption and long-term value.
Identify potential uses for data, automation, analytics, and AI in business and manufacturing processes; define requirements, assess risks, and measure value from digital and AI-enabled solutions.