Director, AI Data Engineering Tech Lead

Pfizer; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Pfizer is seeking an AI Data Engineering and Tech Lead to design, guide, and lead a data engineering team in India (Mumbai/Chennai). You will own end-to-end data product engineering, from user requirements through production deployment, ensuring features are production-grade and aligned with business strategy.

You will mentor a high-performing team, establish deployment standards, and collaborate with global stakeholders to accelerate AI-enabled data products while maintaining quality and

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or related field.
• 10+ years in data engineering, data science, or related technical fields.
• 5+ years leading technical teams with people management responsibilities.
• Strong hands-on experience building and shipping AI ready data products end-to-end.
• Proficiency in SQL, Python with practical experience in Snowflake.
• Experience with cloud platforms (AWS or Azure), containerization (Docker/Kubernetes), and CI/CD (GitHub Actions).
• Experience with AI ready data enablement frameworks and practical implementations, Semantics management and Enterprise data catalogues (Collibra).
• Experience with Enterprise data quality management and observability solutions
• Experience with complex data sources including anonymized patient data (EMR/Claims)
• Strong English communication skills (written and verbal); ability to work across global time zones

Responsibilities

• Own end-to-end engineering delivery of AI-enabled data products from user story refinement to production release.
• Collaborate with Product Owners and stakeholders to ensure features deliver measurable user value.
• Develop parametrized, automated, reusable data/model pipelines for speed and interoperability.
• Lead engineering maturity through ADRs, code reviews, and continuous learning.
• Build and mentor a high-performing data engineering team in India (Mumbai/Chennai).
• Establish CI/CD pipelines, automated testing, and deployment standards for frequent releases.
• Coach team on best practices, security controls, and DevSecOps.

Skills

Strong English communication

Leadership

Data engineering

Education

Bachelor's or Master's degree in Computer Science / Data Engineering / Data Science

MS/PhD in Computer Science / Data Engineering / Data Science

Tools

Python

SQL

Snowflake

AWS

Azure

Docker

Kubernetes

GitHub Actions

Collibra

Job description

Join Pfizer International Commercial Division, Business Transformation organization to leverage cutting-edge technology for critical business decisions and enhance customer experiences for colleagues, patients, and physicians. Our team of data engineering, data science, and AI professionals is at the forefront of Pfizer's transformation into a digitally driven organization, using data science and AI to change patients' lives, leading process and engineering innovations to advance AI and data science applications from prototypes and MVPs to full production.

As the AI Data Engineering and Tech Lead you will design, guide and lead the engineering team to provide reliable and stable AI ready data products and solutions. Your team will architect and develop shared artifacts, tooling and deployment standards that accelerate AI enabled data products.

This role is the technical anchor for a portfolio of foundational data products as priorities and defined by the business strategy, owning end-to-end data product engineering from user requirements through production deployment.

This is a product-focused technical leadership role. You will ensure that every feature shipped meets user expectations, is production-grade, and is built on solid engineering practices. You also build and mentor a high-performing data engineering team in India (Mumbai/Chennai) that delivers with speed, quality, and autonomy.

1) AI Data Engineering Vision

• - Own the end-to-end engineering delivery of AI enabled data products in the portfolio: from user story refinement and technical design through development, testing, and production release
• - Drive data product vision alignment by partnering with Product Owners and commercial stakeholders to ensure every feature delivers measurable user value
• - Enable parametrized, automated, and reusable data/model pipelines that accelerate feature delivery and ensure interoperability across the analytics ecosystem
• - Stay current with emerging AI data engineering technologies and evaluate their applicability to International Commercial product roadmap

2) Application Development & Stakeholder Collaboration

• - Partner with global commercial teams, brand leads, and regional stakeholders to deeply understand user workflows, pain points, and unmet needs
• - Translate user requirements into technical specifications, ensuring alignment between business intent and engineering execution
• - Drive iterative development cycles with rapid prototyping, user feedback loops, and continuous improvement
• - Coordinate with enterprise Data and AI Platform teams to leverage shared infrastructure while maintaining product delivery velocity

3) Engineering Excellence & Quality

• - Establish and maintain CI/CD pipelines, automated testing, and deployment standards that ensure reliable, frequent releases
• - Define quality standards including test coverage targets, release readiness criteria, and production monitoring
• - Leverage observability tools to gain insights into system behavior and proactively address issues
• - Champion DevSecOps practices: embed security controls and compliance checks into development workflows

4) People Leadership & Team Development

• - Build and mentor a high-performing team of AI data engineers
• - Set technical direction, career paths, and coaching routines; foster a culture of ownership, learning, and engineering excellence
• - Coach direct reports to adopt best practices, improve technical skills, and achieve professional growth
• - Lead contractor and vendor support to extend capabilities and maximize delivery efficiency

Drive engineering maturity through design docs, architecture decision records (ADRs), code reviews, and continuous learning (labs, guilds, demos)

This role covers a broad spectrum of skills and we encourage you to apply even if you meet partially.

BASIC QUALIFICATIONS

• - Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or related field
• - 10+ years in data engineering, data science, or related technical fields
• - 5+ years leading technical teams with people management responsibilities
• - Strong hands-on experience building and shipping AI ready data products end-to-end
• - Proficiency in SQL, Python with practical experience in Snowflake
• - Experience with cloud platforms (AWS or Azure), containerization (Docker/Kubernetes), and CI/CD (GitHub Actions)
• - Experience with AI ready data enablement frameworks and practical implementations, Semantics management and Enterprise data catalogues (Collibra)
• - Experience with Enterprise data quality management and observability solutions
• - Experience with complex data sources including anonymized patient data (EMR/Claims)
• - Strong English communication skills (written and verbal); ability to work across global time zones

PREFERRED QUALIFICATIONS

• - Advanced degree (MS/PhD) in Computer Science, Data Engineering, or Data Science
• - Experience with full-stack web development (React, Vue; HTML, Tailwind CSS, Bootstrap)
• - Experience with dat