Director and Principal Lead - Data Engineering

Astrazeneca India Private Limited; Chennai; Hybrid; INR 4,500,000 - 7,000,000; Full time

Benefits offered by this job

Hybrid work model

In-office three days per week

Job summary

AstraZeneca in Chennai seeks a Director - Principal Data Engineering Lead to shape and drive the Data and AI strategy for drug development. You will fund platform investments, define capability roadmaps, and translate strategy into scalable, compliant solutions across enterprise workflows.

You will lead cross-functional teams, promote reusable patterns, and ensure integration with legacy and regulated systems while maintaining strong governance and security practices.

Qualifications

• Experience delivering AI-native products in enterprise environments.
• Translate GenAI research into scalable industrial solutions.
• Hands-on cloud/data architecture and modern patterns.
• Secure software engineering practices.
• Experience mentoring engineers and leading teams.
• Ability to influence senior partners and shape technology strategy.

Responsibilities

• Shape and evolve long-term Data and AI strategy for drug development.
• Lead multi-functional engineering teams from prototype to production systems.
• Define, govern and promote engineering standards and reusable design patterns.
• Promote data integrity, governance, metadata, lineage and audit trails.
• Establish AI/ML governance controls, risk assessment and oversight.
• Implement production-grade GenAI and retrieval-augmented generation solutions.

Skills

AI-native leadership

GenAI capabilities

Cloud architecture

Python

Mentoring engineers

Stakeholder influence

Security practices

Tools

Docker

Kubernetes

CI/CD

Infrastructure as Code

SQL/NoSQL

Job description

Director - Principal Data Engineering Lead Job Title: Director - Principal Data Engineering Lead GCL: F Location: Chennai

Introduction to role: Are you ready to turn the latest AI breakthroughs into resilient, production-grade systems that accelerate late-stage scientific discovery and move life-changing medicines closer to patients? Can you picture yourself setting enterprise-wide Data and AI strategy while building reusable autonomous frameworks and GenAI capabilities that scale with confidence? In this role, you will lead how we embed intelligence into the digital backbone of drug development. You will develop long-term platform funding priorities, capability roadmaps, and technology choices. Then you will translate strategy into solutions that make complex enterprise workflows simpler, safer, and faster for our scientists and product teams. Join us at a pivotal point in our transformation into a digital and data-led enterprise. Backed by investment and an ecosystem of partners, you will work across AI science, engineering and business partners to create patterns that become the standard and prove them through high-impact deliveries that stand up in a regulated environment.

Accountabilities: Shape and evolve the long-term Data and AI strategy for drug development, including platform investments, capability roadmaps, technology choices and alignment with enterprise architecture. Partner with AI science teams to design reusable agentic patterns, GenAI components and scalable solutions that streamline sophisticated enterprise workflows and accelerate delivery. Influence enterprise architecture decisions and lead strategic vendor evaluations, pilots and ecosystem partnerships to bring the best technologies into the enterprise. Lead multi-functional engineering teams, driving end-to-end delivery from prototype to robust, compliant production systems. Define, govern and promote engineering standards, guidelines and reusable design patterns; produce and review detailed technical designs across data models, APIs, integrations, security, deployment, error handling, performance, monitoring, testing and support. Promote strong data integrity and governance, including clear ownership, quality management, metadata, lineage, audit trails, master and reference data, and standardized terminology. Establish and maintain AI and ML governance controls, covering risk assessment, proper use, human oversight, explainability, model approval, versioning, performance monitoring, change control and retirement. Implement production-grade controls for GenAI and retrieval-augmented generation solutions to deliver reliable, traceable and secure outputs based on trusted information, including prompt and model management, response quality testing, source referencing, access controls and mitigations for prompt attacks and data leakage. Define and operate comprehensive evaluation and monitoring frameworks that track accuracy, reliability, security, response quality, business relevance, degradation, latency, cost and operational stability. Establish API-based and event-driven integration standards, including data formats, contracts, versioning, interoperability and integration with legacy and regulated systems. Improve value from enterprise data platforms and guide the implementation of DevOps, DataOps and MLOps practices to deliver resilient, automated, auditable pipelines across data, ML and AI. Ensure data solutions adhere to FAIR principles—findable, accessible, interoperable and reusable—and support end-to-end machine learning and AI workflows. Foster a culture of continuous improvement, experimentation, collaboration, and innovation across engineering and AI teams, encouraging thoughtful use of AI and digital tools in day-to-day work.

Essential Skills/Experience:

• Track record of leading end-to-end delivery of AI-native products within a controlled or enterprise environment.
• Demonstrable ability translating research advances in GenAI or agent-based systems into scalable industrial solutions.
• Deep knowledge of modern cloud and data architecture patterns: microservices, serverless, etc.
• Hands-on experience with containerization (Docker, Kubernetes), SQL/NoSQL databases, CI/CD, and infrastructure-as-code.
• Fluency in Python, with a good understanding of secure software engineering practices.
• Experience mentoring engineers and leading high-performing teams.
• Comfortable influencing senior partners and shaping technology strategy across functions.

Desirable Skills/Experience:

• Pharma industry experience, particularly in R&D or Late-Stage Development.
• AI or Gen AI technical certifications or equivalent.
• AWS Cloud Certification or equivalent.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

Why AstraZeneca:

Here, your work directly accelerates the development of life-changing medicines by pairing leading science with modern data platforms, analytics and AI. You will join a community that brings unconventional combinations of expertise together to spark bold thinking, supported by real investment and the freedom to experiment—from hackathons to high-impact deliveries in production. We value kindness alongside ambition, recognize meaningful contributions and create space for diverse minds to collaborate, learn fast and shape how an entire enterprise uses data and AI to create patient impact.

Experience Level Executive Level

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.