POSITION SUMMARY

The Commercial Analytics & AI (CAAI) China team is responsible for the delivery of services that require secondary data analysis across business units (BU) to provide strategic recommendations on performance review, brand planning, operation planning, promotional resource allocation, field force sizing, segmentation/targeting, structural decisions, and digital strategies, etc. The team delivers the services by leveraging the central expertise and existing tools, or by building simple to intermediate analysis locally.

ROLE SUMMARY

The Analytics Engineering team in the Commercial Analytics & AI (CAAI) organization serves as the backbone of our data-driven organization, delivering the foundation upon which transformative insights and innovation thrive. With a focus on integration, curation, and quality assurance across diverse data sources, coupled with our expertise in managing data infrastructure and crafting impactful visualizations, we're not just enabling analytics - we're powering world-class solutions that propel Pfizer's mission forward. Join us in shaping the future of advanced analytics and make a meaningful impact on a global scale.

• Design and implement secure and scalable infrastructure tools, data integrations, and automation using modern light-weight technologies that enable data engineering (content development) to create scaled analytics.
• Collaborate with cross-functional teams including Pfizer Enterprise IT to align, standardize, optimize, and scale infrastructure in alignment with Pfizer's standards.
• Own and refine existing continuous integration continuous delivery (CI/CD) processes for build, test, and deployment within the Commercial Analytics Engineering platform stack.
• Work closely with content development teams to streamline workflows and improve efficiency of tools for their use in the local development context, following SDLC best practices.
• Innovate to solve complex problems that require knowledge of containers, APIs, AWS and Azure cloud infrastructure, security, and best practices in infrastructure engineering.
• Develop monitoring solutions and operational process for analytics platforms, ensuring early detection and rapid response to potential issues.
• Participate in incident response activities, troubleshoot problems, and implement preventive measures such as expansion of QA frameworks.
• Collaborate with Data Science and Analytics Engineering teams to understand application requirements and provide effective infrastructure support.
• Maintain comprehensive documentation of infrastructure configurations and processes, including the DevOps runbook.
• Own coding and engineering best practices standards and governance for a multi-faceted set of ELT pipelines that operate on various cadences to satisfy business requirements.
• Implement security best practices to protect analytics environments and ensure compliance with Pfizer's stringent standards.

• Collaborate with our internal and external technical and business partners on design and implementation of efficient data systems; compute utilization and code tuning strategies included.
• Lead and build test automation tools and frameworks to test pipelines.
• Partner with enterprise Digital Engineering team to identify or code APIs that need to be instrumented for data analytics and reporting and align with already established data pipelines.

• Analyze technical debt, legacy architectures, and work with other leads on optimizing design and re-platforming efforts.
• Ramp up quickly in understanding data sources required for data products and the goals of Commercial Analytics in a Pharma business.
• Collaboration with the Analytics Engineering Director to work hand in hand with the Platform/Digital and DevOps team to specify platform requirements, tool enhancements, observability & monitoring, security/audit, and alerting on various data pipelines and jobs as needed for operation the Commercial Analytics Engineering organization.

• Solid Line to: Director, China Data Science & AI Lead

• Bachelor's degree (Master's a plus) in computer science, engineering, or related applicable field.
• 5+ years of development operations and platform engineering experience, working in an agile development team to architect and develop tooling, utilities, and data integrations while mentoring junior developers.
• Recent Healthcare Life Sciences (pharma preferred) and ecosystem professional industry experience is preferred, commercial/marketing experience is a plus.

• Advanced experience in python, sql, linux based OS, git, and infrastructure as code.
• Professional hands-on experience with containers (e.g., Docker, ECS, Kubernetes), cloud platforms (e.g., AWS, Azure), and event-driven architecture is required.
• Professional hands-on experience in design, development, testing/mocking, and deployment of APIs is required.
• Experience with both tradition RDBMS analytics developed through SQL and big data processing technologies (e.g., Apache Spark).
• Advanced experience working in multi-stage environments that leverage automation such as Step Functions, Airflow, Cron, etc.
• Must demonstrate ability to leverage digital diagraming tools and communicate technical requirements in a remote setting.
• Strong understanding of Agile, SDCL, CI/CD, DevOps, GitOps, and ProdOps is required.
• Must have experience working in Jira and Confluence, and be a strong writer, contributing to engineering team documentation/playbooks.
• Prioritizes excellence in data engineering by following F.A.I.R. principals and adheres to engineering and documentation standards set in the organization.
• Professional certification in relevant DevOps tools or cloud platforms.
• Professional experience in system architecture and writing requirements documents.
• Practical hands-on experience in event driven job schedulers, orchestration, and micro-services architecture.
• Hands-on experience with cost-optimization strategies for data analytics and informatics.
• Familiarity with data privacy standards, pharma industry practices/FDA compliance is a plus.
• Experience in developing QA automation frameworks, functional and non-functional, is a plus.
• Familiarity with DBA activities, master data management strategies, rules-based engines, and tools for multi-stage data correlation on large data sets.

• Project Management: Overseeing complex, cross-functional projects, ensuring delivery on time and within budget.
• Technical Strategy: Contributing to the technical strategy and architecture decisions, ensuring scalability and performance of data solutions.
• Collaboration: Influencing their data engineering team, working closely with other departments, understanding their data needs, and delivering solutions that meet these needs.
• Operational Excellence: Ensuring the reliability, efficiency, and quality of data services and pipelines.
• Change Management: Leading change initiatives, improving processes, and implementing new technologies.