Associate Director Data Engineer
Role details
Job location
Tech stack
Job description
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.
What You'll Do
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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.
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Modeling and warehousing: Define canonical data models, dimensional schemas, and lakehouse/warehouse layers; implement semantic modeling; optimize storage, compute, and query performance.
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Data integration: Build and harden ingestion frameworks for structured and unstructured data; standardize metadata, lineage, and cataloging; ensure interoperability across domains.
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Governance and quality: Establish and enforce standards for data quality, access control, retention, and compliance; implement monitoring, observability, and automated data quality checks.
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Infrastructure engineering: Operate solutions across Unix/Linux HPC and cloud (AWS preferred), leveraging infrastructure-as-code to ensure reliability, scalability, and cost efficiency.
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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.
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Engineering excellence: Apply software engineering best practices (version control, CI/CD, automated testing, design patterns, code review) to deliver maintainable, resilient systems.
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Enablement: Produce high-quality documentation, reusable components, and guidance; mentor engineers and uplift data engineering practices across teams.
Requirements
Do you have experience in Terraform?, Do you have a Master's degree?, * Education: Degree in Computer Science, Engineering, or related field, or equivalent industry experience.
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Programming: Strong Python expertise; familiarity with Java or C++; ability to write clean, testable, performant code.
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Platform architecture: Proven experience architecting and building data platforms and data-driven solutions at scale.
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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.
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Data modeling and warehousing: Experience with dimensional modeling, semantic layers, and warehouse/lakehouse technologies (e.g., Snowflake, Databricks, TileDB).
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Databases: Hands-on experience with SQL and NoSQL systems, query optimization, and performance tuning.
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Compute environments: Practical experience with Unix/Linux HPC and cloud platforms (AWS preferred), including infrastructure-as-code (e.g., Terraform/CloudFormation).
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Translation of needs: Ability to convert scientific/business requirements into robust technical solutions with measurable outcomes.
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Technical leadership: Demonstrated experience leading end-to-end delivery, setting engineering standards, and guiding teams while remaining hands-on.
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Core skills: Excellent problem-solving, analytical, and critical-thinking capabilities; attention to detail; strong communication and stakeholder management skills.
Desirable Skills & Experience
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Generative and agentic AI: Exposure to LLM-enabled data services or agentic workflows.
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Data processing and integration: Experience integrating structured and unstructured data at scale; familiarity with streaming and batch patterns.
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Life sciences: Experience with clinical or pre-clinical drug discovery, imaging and bioinformatics data; understanding of domain ontologies and scientific data standards.
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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.
