Associate Director Data Engineer
Role details
Job location
Tech stack
Job description
Here, the answers aren't always available. So, you'll need to bring a fearless, self-starter mindset to navigate uncharted territories. You'll harness your ceaseless energy to discover and make the necessary connections with colleagues to shape the future and achieve maximum impact., 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
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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.
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Modeling and warehousing:Define canonical data models, dimensional schemas, andlakehouse/warehouse layers; implement semantic modeling;optimizestorage, 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:Establishand enforce standards for data quality, access control, retention, and compliance; implement monitoring, observability, and automated data quality checks.
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Infrastructure engineering:Operatesolutions across Unix/Linux HPC and cloud (AWS preferred),leveraginginfrastructure-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., Your wellbeing means a lot to us, and we're here to support you through all of life's ups and downs. That's why we offer an unpaid leave policy, annual leave, reduced-hours timetables and a host of benefits, including a retirement plan, long service award, and health and travel insurance.
Requirements
Ready to make an impact in your career? If you're passionate, growth-orientated and a true team player, we'll help you succeed. Here are some of the skills and capabilities we look for., Seize ownership and excel with autonomy to enjoy the constant rush of ground-breaking discovery. Your ability to anticipate sudden shifts and adapt swiftly will prove critical as you make your mark in an environment that rewards initiative and resilience., * Education:Degree in Computer Science, Engineering, or related field, or equivalent industry experience.
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Programming:Strong Pythonexpertise; 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;proficiencywith 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/lakehousetechnologies (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:Demonstratedexperience leading end-to-end delivery, setting engineering standards, and guiding teams whileremaininghands-on.
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Core skills:Excellent problem-solving, analytical, and critical-thinking capabilities; attention to detail;strong communicationand stakeholder management skills.
Desirable Skills & Experience
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Generative and agentic AI: Exposure toLLM-enabled data services or agentic workflows.
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Data processing and integration: Experience integratingstructured and unstructured dataat scale; familiarity with streaming and batch patterns.
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Life sciences:Experience with clinical or pre-clinical drug discovery,imagingand 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.