Associate Director Data Engineer

AstraZeneca is transforming into an AI- and data-led enterprise. Within R&D, thePredictive AI & Datateam turns complex information into practical, life-changing insights that improve patient outcomes. We invent, build, and deliver novel solutions alongside leading experts,leveragingcutting-edgetechniques in data, AI, and machine learning. We work inclusively across diverse disciplines and partners,aligning tobusiness needs and delivering measurable value.

We are seeking a hands-onAssociate Director of Data Engineeringto lead data architecture, modeling, warehousing, and platform engineering that accelerates scientific decision-making across Clinical Pharmacology & Safety Science (CPSS). You will design and deliver scalable, FAIR-aligned data solutions on enterprise infrastructure, driving positive, disruptive transformation toward AstraZeneca’s Bold Ambition for 2030. This role partners closely with R&D IT and DS&AI and collaborates globally with colleagues in Sweden, the United Kingdom, and the United States.

WhatYou’llDo

• Data platform architecture:Design, implement, andoperaterobust, secure, and scalable data platforms and services that enable discovery, access, and reuse (FAIR), with clear SLOs for reliability and performance.

• Modeling and warehousing:Define canonical data models, dimensional schemas, andlakehouse/warehouse layers; implement semantic modeling;optimizestorage, compute, and query performance.

• Data integration:Build and harden ingestion frameworks for structured and unstructured data; standardize metadata, lineage, and cataloging; ensure interoperability across domains.

• Governance and quality:Establishand enforce standards for data quality, access control, retention, and compliance; implement monitoring, observability, and automated data quality checks.

• Infrastructure engineering:Operatesolutions across Unix/Linux HPC and cloud (AWS preferred),leveraginginfrastructure-as-code to ensure reliability, scalability, and cost efficiency.

• Collaboration:Translate scientific and business requirements into well-architected designs; co-create solutions with CPSS stakeholders, R&D IT, and DS&AI; set technical direction and roadmap.

• Engineering excellence:Apply software engineering best practices (version control, CI/CD, automated testing, design patterns, code review) to deliver maintainable, resilient systems.

• Enablement:Produce high-quality documentation, reusable components, and guidance; mentor engineers and uplift data engineering practices across teams.

Essential Skills & Experience

• Education:Degree in Computer Science, Engineering, or related field, or equivalent industry experience.

• Programming:Strong Pythonexpertise; familiarity with Java or C++; ability to write clean, testable, performant code.

• Platform architecture:Proven experience architecting and building data platforms and data-driven solutions at scale.

• Software engineering:Track record delivering production-grade systems in data, AI, or scientific domains;proficiencywith Git, CI/CD, automated testing, design patterns, and DevOps/SRE practices.

• Data modeling and warehousing:Experience with dimensional modeling, semantic layers, and warehouse/lakehousetechnologies (e.g., Snowflake, Databricks,TileDB).

• Databases:Hands-on experience with SQL and NoSQL systems, query optimization, and performance tuning.

• Compute environments:Practical experience with Unix/Linux HPC and cloud platforms (AWS preferred), including infrastructure-as-code (e.g., Terraform/CloudFormation).

• Translation of needs:Ability to convert scientific/business requirements into robust technical solutions with measurable outcomes.

• Technical leadership:Demonstratedexperience leading end-to-end delivery, setting engineering standards, and guiding teams whileremaininghands-on.

• Core skills:Excellent problem-solving, analytical, and critical-thinking capabilities; attention to detail;strong communicationand stakeholder management skills.

Desirable Skills & Experience

• Generative and agentic AI: Exposure toLLM-enabled data services or agentic workflows.

• Data processing and integration: Experience integratingstructured and unstructured dataat scale; familiarity with streaming and batch patterns.

• Life sciences:Experience with clinical or pre-clinical drug discovery,imagingand bioinformatics data; understanding of domain ontologies and scientific data standards.

• Governance and compliance:Experience with data governance, privacy, security-by-design, and relevant regulatory frameworks.

Ways of Working :

We value in-person collaboration to accelerate learning and decision-making. We typically work a minimum of three days per week from the office while balancing flexibility for individual needs.

Why AstraZeneca:

We follow the science to explore and innovate, fusing data and technology with the latest scientific advances to achieve the next wave of breakthroughs. We listen and learn from people living with the diseases we treat to better understand needs and design the right interventions. If your passion is science andimpact onpatients’ lives, this is the place to build a career that matters.

Ready to make an impact? Apply now and join us in shaping the future of data architecture and infrastructure at AstraZeneca.