Lead Consultant - Data Engineering

Job Title: Lead Consultant - Data Engineering Introduction to role: Are you ready to turn complex, global data into decisions that transform how a science-led business operates at scale? Step into a role where your engineering leadership powers the data foundations behind faster discovery, smarter development and better access for patients. You will join a global technology community focused on data, analytics and AI, working across data platforms, cloud, software engineering and emerging AI practices. Partnering closely with product leaders and architects, you will shape and deliver new enterprise-grade solutions for multiple business areas—translating strategy into production systems that teams rely on every day. This is a pivotal moment in our journey to become truly data-led. You will set the technical direction, stay hands-on with engineering, and mentor a diverse team to deliver reliable, secure and scalable data products at pace. Could you connect the dots across platforms and people to build systems that move the needle for patients and the business? Accountabilities: Data Platform Architecture: Design, develop and lead scalable, robust data infrastructure that underpins analytics and AI, ensuring solutions are secure, cost‑effective and built for change. End-to-End Delivery Leadership: Act as the technical single point of accountability from requirements through deployment and transition to BAU, staying hands-on and driving code quality, test coverage and release readiness. Product and Architecture Alignment: Collaborate with product managers, architects and delivery leaders to define the technical vision and roadmaps that directly support product objectives and measurable business outcomes. Technical Design and Risk Management: Own detailed technical designs and user story refinement, proactively manage technical risks and dependencies, and optimise resource allocation. Team Leadership and Mentoring: Lead and coach multidisciplinary engineering teams (Data Engineering, AI and more), partnering with AI/GenAI leaders to spark collaboration and innovation. Governance and Standards: Define and communicate governance frameworks for Data Engineering, including risk management, performance metrics and reusable patterns that accelerate delivery. Advanced Analytics: Develop and operationalise advanced analytical models, ensuring data pipelines and features are production‑ready, observable and maintainable. Regulatory and Security Compliance: Ensure all solutions meet regulatory requirements, data security policies and industry standards—by design, not as an afterthought. Performance and Reliability: Continuously monitor, tune and improve data systems and analytics solutions to enhance reliability, throughput and cost efficiency at scale. FAIR Data Principles: Embed Findability, Accessibility, Interoperability and Reusability into all data solutions to maximise reuse, sharing and enterprise value. Essential Skills/Experience: Around 10 years in data and software engineering. Deep expertise in Data Engineering, Software Engineering and AI. Proficiency in Data Platforms like Snowflake/Red Shift/Big Query, PL\SQL and ETL Tools (Snaplogic/DBT). Proficiency in writing sophisticated SQL queries, Stored Procedures, and other database-related tasks. Demonstrable understanding of DevOps, DataOps, and product management. Good understanding of Data Mesh and Data Product concepts. Hands on development of Data Models (Star or Snowflake) is a must. Bachelor’s or Master’s degree in a relevant field or equivalent experience. Experience in the pharmaceutical industry or similar large multinational environments. Desirable Skills/Experience: Experience with automation strategies (CI/CD etc) and containerisation (Kubernetes, Docker) will be an advantage. Familiar with DBT, FiveTran and AI Enabled engineering practices. Good to have proficiency in programming languages such as Python, JavaScript etc. Able to develop and integrate applications using APIs will be a significant advantage. Familiarity with cloud platforms like AWS. Knowledge of NoSQL databases. Having experience in developing and deploying AI and machine learning applications will be beneficial. Why AstraZeneca: Here, technology and science meet to deliver visible impact—from accelerating discovery to simplifying operations so medicines reach patients sooner. You will work with modern platforms and bold thinkers, often bringing unexpected teams together to unlock ideas, backed by meaningful investment and room to experiment. We value kindness alongside ambition, champion transparent collaboration and lifelong learning, and encourage you to challenge norms in pursuit of better outcomes. Your contribution will scale across the enterprise, advancing what digital, data and AI can do for healthcare while advancing your craft every day. Call to Action: Seize the chance to lead enterprise data engineering that drives real patient impact—join us to build the platforms, products and practices that will shape our digital future! Date Posted 12-Jun-2026 Closing Date 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 will be.  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.