Director, Analytics Engineering & AI​

ROLE SUMMARY Analytics Gateway, part of Pfizer's Global Commercial Analytics (GCA) organization, is the India-based Global Capability Center that delivers innovative, high-quality analytics solutions at scale to Pfizer's data-driven International Commercial Division (ICD). The Director, Analytics Engineering & AI leads Analytics Gateway’s Data Engineering and Business Intelligence/Data Visualization teams which consists of ~20 colleagues who, in addition to traditional Data Engineering and Data Visualization offerings, are increasingly focused on designing, building, and scaling AI-enabled analytics solutions. Reporting to the Mumbai-based lead of Analytics Gateway, the Director will oversee the execution and delivery of analytics solutions and will work with International Commercial Division stakeholders and Business Transformation and Chief Marketing Office teams to ensure Gateway’s technical capabilities continue to meet the business's evolving needs. Lead the ~20 colleagues that form the AI Engineering, Data Engineering, and Business Intelligence / Data Visualization teams within the Mumbai-based Analytics Gateway Global Capability Center supporting Pfizer's International Commercial Division. Manage 4–6 direct reports and oversee hiring, colleague development, and resource allocation across the team. Act as the AI Engineering lead , owning the design, build, deployment, and scaling of AI-enabled and agentic solutions for commercial use cases, from proof of concept to production. Serve as solution architect and design authority for the team, defining reference architectures, technical standards, and reusable patterns across data, AI, and BI solutions, and providing technical sign-off on solution designs. Lead the development of agentic AI solutions using Snowflake Cortex Agents , orchestrating across structured and unstructured commercial data to deliver conversational, self-service insight and automated workflows, with appropriate guardrails, access controls, and responsible AI practices. Establish and own MLOps / LLMOps practices , including model and agent versioning, CI/CD for AI, automated testing and evaluation, performance and cost monitoring, and lifecycle management to ensure solutions are reliable, compliant, and production-grade. Design and deliver strategic analytics solutions end to end, sourcing and curating data from diverse commercial data sources and applying established data engineering practices alongside emerging AI techniques. Own the evolution of the team's data foundation, from data curation, database modeling, and ETL pipeline development toward governed, observable, AI-ready data layers, driving data quality and permanent resolution of issues at the source. Deliver intuitive, AI-enabled decision-support and reporting tools that turn complex data into clear, actionable insight for commercial stakeholders, from country teams to global leadership. Guide the team's transition from traditional BI and data engineering skillsets toward AI engineering, agentic AI, and modern data engineering, including infrastructure standardization, automation, and CI/CD, supported by talent pipelines, training, and mentorship. Partner with Business Transformation & Technology, Data Science, and Insights & Strategy teams to align on foundational data assets, engineering and AI best practices, platform choices, and shared roadmaps supporting Commercial's AI and analytics agenda. Represent the team to GCA and ICD leadership, providing visibility into delivery, capability growth, and risk, and translating technical outcomes into clear business narratives. Contribute to documentation, playbooks, and knowledge-sharing practices, serving as a subject matter expert who raises AI and analytics capability across the organization. Drive a positive, inclusive, and high-engagement culture focused on retention, innovation, team development, and knowledge sharing. BASIC QUALIFICATIONS 15-20 years of relevant experience in data analytics, data science, or business intelligence, and 8&#43; years’ people-management experience with demonstrated success leading managers. Proven track record of leading the design, development, and delivery of AI-enabled analytics and data engineering solutions that drive measurable business impact at scale. Education: Advanced degree in Computer Science, Data Engineering, Management, Applied Econometrics, Actuarial Science, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field. PREFERRED QUALIFICATIONS Demonstrated breadth of technical leadership experience, including engineering delivery, data architecture, ETL pipeline development (e.g., dbt, Airflow), database modeling, and BI/visualization tooling (e.g., Tableau, Power BI), together with fluency in data quality/observability monitoring (e.g., Grafana), agentic AI, and modern cloud data platforms (e.g., Snowflake) Strong ability to influence stakeholders to develop high‑performing teams by coaching direct-report workstream leads to deliver meaningful outcomes and sustained business impact. Hands-on experience architecting and delivering AI / agentic solutions on Snowflake Cortex (Cortex Agents, Cortex Analyst, Cortex Search) or similar platforms. Experience establishing MLOps / LLMOps practices, including model and agent deployment, evaluation, monitoring, and governance. <span style="color