New opportunity

Director, Analytics Engineering & AI

Pfizer · Mumbai, India

About this role

The Director, Analytics Engineering & AI leads Analytics Gateway’s Data Engineering and Business Intelligence/Data Visualization teams at Pfizer's Mumbai-based Global Capability Center, supporting the International Commercial Division (ICD). This role oversees ~20 colleagues focused on designing, building, and scaling AI-enabled and agentic analytics solutions from proof of concept to production.

Responsibilities

Lead the ~20 colleagues forming 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.

Required qualifications

15-20 years of relevant experience in data analytics, data science, or business intelligence, and 8+ 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.
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.
Proven experience as a solution architect for enterprise-scale data and AI platforms.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI EngineeringData EngineeringBusiness IntelligenceData VisualizationSnowflake Cortex AgentsMLOpsLLMOpsdbtAirflowTableauPower BIGrafanaSnowflakeCortex AnalystCortex Search

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Scaling Agentic AI Solutions
Lead the design, development, and deployment of agentic AI and analytics solutions utilizing tools like Snowflake Cortex Agents to orchestrate structured and unstructured commercial data, driving self-service insights and automated workflows with proper governance.
Team Leadership and Skill Transition
Directly manage a ~20-person organization including 4-6 direct reports, guiding the transition of traditional BI and data engineering teams toward modern AI engineering practices through training, mentorship, and standardized workflows.
Establishing MLOps and LLMOps Governance
Own and establish robust MLOps and LLMOps practices encompassing model and agent versioning, CI/CD pipelines, automated testing, cost and performance monitoring, and compliance for production-grade deployments.
Evolving the Data Foundation
Drive the evolution of the team's underlying data architecture from standard ETL pipelines and database modeling toward governed, observable, and AI-ready data layers that ensure high data quality at the source.

Likely challenges to think through

Balancing Traditional BI with Cutting-Edge Agentic AI
A key challenge involves uplifting a legacy team accustomed to traditional data engineering and dashboarding into modern agentic AI engineering without disrupting ongoing commercial analytics delivery. Prepare by sharing a structured enablement roadmap and examples of phased skill migration.
Ensuring Robust Compliance and Guardrails in Agentic Workflows
Deploying conversational, automated agentic workflows over sensitive commercial data requires strict access controls and responsible AI practices to prevent hallucinations or data leaks. Prepare by discussing specific MLOps, testing, and guardrail methodologies implemented on cloud platforms.

Practice your answers

Technical practice

Designing Enterprise Reference Architectures for AI and Data

What to coverDiscuss how you define reference architectures, technical standards, and reusable patterns across data engineering, AI, and business intelligence. Candidates should address balancing standardization with flexibility, ensuring technical sign-off on solution designs, and establishing enterprise-scale governance.

Implementing Agentic AI Solutions with Snowflake Cortex

What to coverExplain how you design agentic AI workflows using Snowflake Cortex Agents, Cortex Analyst, and Cortex Search. Focus on how you orchestrate across structured and unstructured commercial data to deliver self-service conversational insights while maintaining appropriate guardrails and access controls.

Establishing MLOps and LLMOps Lifecycle Management

What to coverDescribe your approach to setting up MLOps and LLMOps practices for production-grade AI applications. Address model and agent versioning, CI/CD for AI, automated testing and evaluation, and ongoing performance and cost monitoring.

Transitioning Traditional BI and Data Engineering Teams toward AI

What to coverHow would you guide a ~20-person team from traditional BI and data engineering toward AI engineering and modern cloud data practices? Candidates should discuss structuring talent pipelines, targeted training, mentorship, and infrastructure standardization.

Building Governed, AI-Ready Data Layers

What to coverExplain your strategy for evolving a team's data foundation from traditional ETL pipeline development (e.g., using dbt and Airflow) and database modeling toward governed, observable, AI-ready data layers that ensure permanent resolution of data quality issues.

Ensuring Data Quality and Observability with Grafana and Modern Tools

What to coverDiscuss how you integrate data quality and observability monitoring into your engineering pipelines. Candidates should explain how tools like Grafana help track system reliability, data drift, and pipeline performance across diverse commercial sources.

Delivering Intuitive AI-Enabled Decision-Support Reporting

What to coverHow do you translate complex data and AI-generated outputs into clear, actionable decision-support tools for stakeholders ranging from local country teams to global leadership using BI tools like Tableau or Power BI?

Balancing Governance and Innovation in Responsible AI

What to coverDiscuss the trade-offs between rapid AI innovation and maintaining strict responsible AI practices, access controls, and regulatory compliance in a global commercial environment.

Behavioural practice

Scaling an AI Analytics Capability in a Global Capability Center

What to coverDescribe a situation where you led the end-to-end design, delivery, and scaling of an AI-enabled analytics solution from proof of concept to production. Share the situation, your specific leadership actions as an architect and mentor, and the resulting business impact.

Coaching and Developing Workstream Leads and Managers

What to coverProvide an example of how you managed direct reports and coached workstream leads to build high-performing, inclusive teams. Outline the specific challenge, your coaching and delegation actions, and the impact on team retention and execution.

Aligning Technical Strategy Across Cross-Functional Stakeholders

What to coverShare an experience partnering with diverse cross-functional groups (such as Business Transformation, Data Science, and Commercial Strategy) to align on shared technical roadmaps. Detail the context, your stakeholder engagement approach, and the final outcome.

Managing Major Technical and Delivery Risks

What to coverDiscuss a time when an enterprise analytics or AI delivery faced significant technical roadblocks or capability gaps. Explain the situation, how you provided visibility to leadership and translated technical outcomes into business narratives, and how you resolved the risk.

Sharpen your knowledge

Topics to prepare

Enterprise AI Solution Architecture
Review reference architectures, technical standards, and governance patterns for scaling AI-enabled and agentic analytics solutions from proof of concept to production. Relevance: Directly tied to the posting requirement to serve as solution architect and design authority defining reference architectures and technical standards.
MLOps and LLMOps Practices
Examine model and agent versioning, CI/CD for AI, automated testing, evaluation, lifecycle management, and performance and cost monitoring. Relevance: Essential for establishing and owning MLOps and LLMOps practices to ensure reliable and compliant production-grade solutions as specified in the role.
Data Foundation and AI-Ready Data Layers
Study data curation, database modeling, ETL pipeline development, and data quality observability to build governed, observable data layers. Relevance: Aligns with the responsibility to own the evolution of the team's data foundation toward AI-ready data layers.
Commercial Analytics Leadership and Team Development
Explore strategies for leading ~20 colleagues across AI, data engineering, and BI teams, managing direct-report leads, and driving capability transitions. Relevance: Directly supports managing 4-6 direct reports, overseeing ~20 colleagues, and guiding the transition toward AI engineering skillsets.

Skills to revise

Snowflake Cortex Agents
Practice architecting and delivering agentic AI solutions orchestrating structured and unstructured commercial data using Snowflake Cortex Agents, Cortex Analyst, and Cortex Search. Relevance: Specifically highlighted in preferred qualifications and core responsibilities for developing conversational, self-service insights.
dbt and Airflow
Review ETL pipeline development and data orchestration using dbt and Airflow for modern cloud data platforms. Relevance: Mentioned in preferred qualifications as part of engineering delivery and data architecture experience.
Grafana Observability
Examine data quality and observability monitoring tools such as Grafana. Relevance: Explicitly noted in preferred qualifications for monitoring data quality and pipeline performance.
Tableau and Power BI
Review BI and visualization tooling to deliver intuitive decision-support and reporting tools. Relevance: Listed in preferred qualifications and responsibilities for supporting commercial stakeholders across country teams to global leadership.

Your preparation checklist

Prepare Enterprise AI and Data Architecture Walkthrough
Develop a comprehensive architectural presentation detailing how you have designed, built, and scaled AI-enabled and agentic analytics solutions from proof of concept to production, emphasizing reference architectures and technical standards.
Draft MLOps and LLMOps Governance Framework
Prepare a document outlining your approach to model and agent versioning, CI/CD for AI, automated testing, cost and performance monitoring, and compliance lifecycle management for enterprise-scale deployments.
Build an Agentic AI Prototype Outline
Create a technical blueprint demonstrating how to orchestrate structured and unstructured data using platforms like Snowflake Cortex Agents, Cortex Analyst, and Cortex Search to deliver self-service insights with secure guardrails.
Map Out Data Foundation and Modernization Strategy
Prepare a structured plan for evolving data curation, database modeling, and ETL pipelines (using tools like dbt and Airflow) toward governed, observable, AI-ready data layers.
Formulate Team Transition and Leadership Playbook
Draft a mentorship and talent development strategy outlining how you guide traditional BI and data engineering teams toward modern AI engineering skillsets while managing direct-report workstream leads.
Prepare Thoughtful Questions for the Employer
Formulate targeted questions regarding how Analytics Gateway collaborates with Pfizer's International Commercial Division, business transformation teams, and global leadership to scale AI and analytics solutions.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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