Senior Data Engineer- Data Architecture & Infrastructure

Job Title: Senior Data Engineer- Data Architecture & Infrastructure

Global Career Level: D2

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 Senior Data Engineer focused on data architecture, modeling, warehousing, and platform engineering to accelerate scientific decision-making across Clinical Pharmacology & Safety Science (CPSS). You will design and deliver scalable, FAIR data solutions on enterprise infrastructure, driving positive disruptive transformation aligned to 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 and implement robust, secure, and scalable data platforms and services that enable discovery, access, and reuse (FAIR).

• Modeling and warehousing: Develop canonical data models, dimensional schemas, and lakehouse/warehouse layers; optimize storage and query performance.

• Data integration: Build reliable ingestion frameworks for structured and unstructured data; standardize metadata, lineage, and cataloging.

• Governance and quality: Establish standards for data quality, access control, retention, and compliance; implement monitoring and observability.

• Infrastructure engineering: Operate solutions across Unix/Linux HPC and cloud environments (AWS preferred); ensure reliability, cost efficiency, and scalability.

• Collaboration: Translate scientific and business requirements into architectural designs; partner with CPSS stakeholders, R&D IT, and DS&AI to co-create solutions.

• Engineering excellence: Apply version control, CI/CD, automated testing, design patterns, and code review to ensure maintainability and resilience.

• Enablement: Produce documentation, reusable components, and guidance to uplift data engineering practices across teams.

Essential Skills/Experience:

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

• Minimum 8+ years of relevant experience.

• Programming: Strong Python skills; familiarity with Java or C++.

• Platform architecture: Experience architecting and building data platforms and data-driven solutions.

• Software engineering: Proven delivery of scalable, production-quality systems in data, AI, or scientific domains; proficiency with version control, CI/CD, automated testing, design patterns, and DevOps.

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

• Databases: Experience with both SQL and NoSQL systems.

• Compute environments: Hands-on experience with Unix/Linux HPC systems and cloud platforms (AWS preferred).

• Translation of needs: Demonstrated ability to convert scientific/business requirements into robust technical solutions.

• Core skills: Excellent problem-solving, analytical, and critical-thinking skills; attention to detail and strong communication and interpersonal 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 data at scale; familiarity with streaming and batch patterns.

• Life sciences: Experience in clinical or pre-clinical drug discovery.

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

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, data, technology, and science meet to do things that have never been done before. You will collaborate with people who value kindness alongside ambition, with the freedom to explore unknowns and the support to learn fast. We unite diverse expertise across disease areas and external partnerships to move from discovery to real clinical impact, bringing unexpected teams into the same room to unleash bold thinking. Your platforms will help predict clinical success, accelerate a productive pipeline, and contribute to better health outcomes for people worldwide.

Call to Action:

Step into this role and build the data foundation that accelerates breakthroughs—take the next step today and shape what science can do.

Date Posted

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.