Job Description

Job Title: Associate Director,

Data Engineering

Global Career Level: E

About our Team:

AstraZeneca is transforming into an AI- and data-led enterprise. Within R&D, our

Predictive AI & Data

team connects expertise across functions to turn complex information into practical, life-changing insights that improve patient outcomes. We invent, build, and deliver novel solutions alongside leading experts, leveraging cutting-edge techniques in data, AI, and machine learning. We work inclusively across diverse disciplines and partners, pooling knowledge to decode business needs, and applying our technical knowledge to deliver value.

Introduction to role:

We are seeking a hands-on

Associate Director of Data Engineering

to 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.

Accountabilities:

Data platform architecture:

Design, implement, and operate robust, 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, and lakehouse/warehouse layers; implement semantic modeling; optimize storage, 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:

Establish and enforce standards for data quality, access control, retention, and compliance; implement monitoring, observability, and automated data quality checks.

Infrastructure engineering:

Operate solutions across Unix/Linux HPC and cloud (AWS preferred), leveraging infrastructure-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.

Minimum 10+ years of relevant experience.

Programming:

Strong Python expertise; 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; proficiency with Git, CI/CD, automated testing, design patterns, and DevOps/SRE practices.

Data modeling and warehousing:

Experience with dimensional modeling, semantic layers, and warehouse/lakehouse technologies (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:

Demonstrated experience leading end-to-end delivery, setting engineering standards, and guiding teams while remaining hands-on.

Core skills:

Excellent problem-solving, analytical, and critical-thinking capabilities; attention to detail; strong communication and stakeholder management skills.

Desirable Skills/Experience:

Generative and agentic AI

: Exposure to LLM*-enabled data services or agentic workflows.

Data processing and integration

: Experience integrating structured and unstructured dataat scale; familiarity with streaming and batch patterns.

Life sciences:

Experience with clinical or pre-clinical drug discovery, imaging and 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.

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, you'll blend cutting-edge engineering with real-world science to push the boundaries of what's possible--fusing data, technology, and bold ideas to accelerate breakthroughs against complex diseases. We bring unexpected teams together, learn relentlessly, and support each other with kindness alongside ambition. You'll work on challenges that have never been solved before, grow through varied opportunities, and see the impact of your tools in publications, decisions, and medicines that reach patients worldwide.

Call to Action:

If you're ready to lead engineering that turns scientific curiosity into life-changing impact, take the next step and show us how you will build the tools that speed breakthroughs to patients!