Senior Manager - Custom Analytics

Job description

Job Title: Senior Manager - Custom Analytics GCL: E Introduction to role: Are you ready to lead the shift from low-code to scalable pro-code analytics that power critical decisions and, ultimately, better patient outcomes? As the lead in Custom Analytics management, you will be the hands-on engineering and change leader crafting a high-performance, enterprise-grade analytics application used across our global operations. You will champion AI-assisted development with a human-in-control ethos—accelerating delivery while safeguarding design quality, security, and compliance. Working closely with technical partners and multi-functional collaborators, you will translate complex requirements into well-structured implementations. These will involve working with React, TypeScript, Python alongside FastAPI, APIs, quality assurance, and continuous integration and delivery. Can you picture guiding teams through this transformation while leaving behind reusable patterns, frameworks, and guides that improve efficiency and quality across the board? We are in a period of rapid growth and digital reinvention, where smarter engineering frees scientists and business colleagues to focus on discovery, insight, and decisions. Your work will streamline the process through which we develop analytics, reduce cycle time across the SDLC, and drive consistent, standards-aligned delivery that scales. Accountabilities: Front-End Engineering: Develop reusable UI components using React + TypeScript + Ant Design to create a consistent, maintainable user experience that accelerates feature delivery. Backend Services: Build and maintain backend services using FastAPI (Python) to deliver reliable, secure, and scalable business capabilities. API Build and Integration: Implement RESTful APIs following defined standards and guidelines, enabling interoperability and performance across services. Data Engineering: Handle data transformation, aggregation, and integration tasks to ensure accurate, timely insights for analytics use cases. Engineering Standards: Follow established system architecture and coding standards to ensure maintainability, compliance, and consistency with organizational frameworks. Quality and Validation: Ensure code quality through testing, validation, and alignment with standards, growing confidence in releases and reducing defects. Performance and Debugging: Support performance optimization and debugging across the application to improve user experience and system resilience. DevOps and Delivery: Collaborate using Docker and continuous integration and delivery pipelines to ensure smooth deployment processes and faster, reliable releases. Risk and Issue Management: Advance and communicate technical risks or blockers effectively to reduce delivery friction and protect timelines. AI-Assisted Delivery: Use approved AI coding assistants such as GitHub Copilot, Claude Code, or equivalent tools to accelerate coding, refactoring, documentation, testing, debugging, and solution analysis while maintaining human oversight. Coaching and Capability Building: Coach low-code/no-code developers into pro-code practices through upskilling, paired development, reusable templates, coding standards, and guided AI-assisted workflows. Responsible AI Guardrails: Apply guardrails for AI-generated code, including human review, testing, security validation, documentation, and alignment with enterprise architecture standards. Pro-Code Translation: Translate low-code/no-code prototypes into scalable pro-code solutions, balancing rapid prototyping with maintainability, extensibility, performance, and governance. Patterns and Templates: Create reusable patterns, repository guidance, starter templates, and playbooks that help AI tools produce consistent, standards-aligned outputs. Change Management: Support AI-assisted development change management through awareness, enablement, office hours, quick-start guides, feedback loops, adoption tracking, and reinforcement. Cross-Functional Partnership: Partner with leads, BAs, solution teams, and developers to reduce SDLC effort through AI across requirements, design, build, test, deployment, and support. Measurement and Reporting: Track adoption, productivity, quality outcomes, and risks from AI-assisted practices, and share progress, blockers, and lessons learned with stakeholders. Essential Skills/Experience: Strong experience with: React (v18+) + TypeScript Python (FastAPI or similar frameworks) Solid understanding of: REST API design and integration Asynchronous programming Component-based architecture Experience with Redux Toolkit or similar state management tools Strong SQL skills and experience with relational databases Ability to work with data structures involving aggregation and hierarchy Understanding of: Clean code practices Scalable application design Strong debugging and problem-solving skills Hands-on experience or strong knowledge of AI-assisted development tools such as GitHub Copilot, Claude Code, ChatGPT Enterprise, or approved equivalents. Ability to use AI across the SDLC for code generation/explanation, refactoring, unit tests, test cases, debugging, documentation, and implementation planning. Strong understanding of responsible AI-generated code use, including review, hallucination checks, dependency validation, privacy, and security vulnerability checks. Experience moving teams from low-code/no-code to pro-code through capability building, coaching, standards adoption, and change management. Ability to create developer guidance, reusable prompts, coding patterns, repository instructions, and documentation for consistent AI-assisted outputs. Strong engineering m