Data Engineering Lead

You’ll I nfluence T he Lives O f M illions G lobally Do you want to be part of one of the global leading innovators in the biopharmaceutical business in shaping the technology that reinforces everything we do that helps AstraZeneca push the boundaries and turns ideas into life changing medicines ? The Drug Development Data Platform team here at AZ plays a key role introducing process and technology improvements such as Data Mesh , Agile, DevOps and the very latest engineering tools to maximise velocity and business value. Apply Y our E xpertise I n A D ynamic T eam You will join the Drug Discovery Data Platform leadership team, delivering data and digital capabilities across Target Identification and discovery through design, make, test and analyse. This is a hands-on technical leadership role. You will lead multidisciplinary teams delivering trusted data products, analytical products and AI-enabled solutions across the full lifecycle—from discovery and architecture through engineering, deployment, adoption, monitoring and continuous improvement. Your work will include: Building discoverable, interoperable and trusted data products for scientific and business users. Developing AI solutions such as predictive analytics, intelligent search, knowledge assistants, RAG, agentic workflows and automation. Setting standards for data engineering, APIs, analytics, machine learning and generative AI. Providing hands-on technical leadership through architecture, design reviews, prototypes, code contributions, performance optimisation and production support. Improving delivery through Agile, DevOps, DataOps , MLOps and LLMOps practices, automation and self-service capabilities. Working with product managers, architects, data owners, scientists and stakeholders to prioritise opportunities and deliver measurable outcomes. Coaching senior engineers and technical leads and promoting responsible, well-governed use of AI. You will primarily work with our AWS data and analytics ecosystem, while collaborating with teams using Azure. Strong engineering judgement and experience selecting appropriate technologies are essential. Key Responsibilities Technical Leadership and Architecture Define technical direction for data products, analytics platforms and AI-enabled capabilities. Design secure, scalable, resilient and cost-effective architectures for batch, streaming, event-driven, API and AI workloads. Establish reusable patterns for data contracts, domain-owned data products, semantic models, metadata, lineage and self-service. Lead decisions across cloud infrastructure, data storage, processing, orchestration, integration, AI services and application development. Manage technical debt, platform risks, performance, dependencies and cloud costs. Ensure solutions meet relevant standards for security, privacy, compliance, validation, resilience, auditability and responsible AI. Hands-on Engineering Contribute to production-quality code, prototypes and technical investigations, primarily using Python and SQL. Build and review data pipelines, transformation frameworks, APIs, analytical products and AI application components. Develop cloud-native solutions using containers, serverless services, managed platforms, event-driven architectures and infrastructure as code. Establish practices for Git-based development, automated testing, CI/CD, release management, observability and operational support. Apply data quality checks, monitoring, performance testing and secure software supply-chain practices. Support incident investigation, root-cause analysis and continuous improvement. AI and Advanced Analytics Identify valuable and feasible applications of AI and advanced analytics in drug discovery. Engineer production AI solutions covering data preparation, model or foundation-model integration, retrieval, tool use, evaluation, deployment, monitoring and cost management. Move suitable concepts from experimentation into reliable services. Product and Delivery Leadership Translate user and business needs into product outcomes, technical options, delivery plans and success measures. Balance speed with quality, maintainability, security, com