{"id":3798100,"url":"https://alion.io/job/astrazeneca-associate-director-data-engineer-2","title":"Associate Director, Data Engineer","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":93,"open_postings":167,"ghost_share":0.012,"stale_share":0.383,"repost_share":0.108,"time_to_fill_p50_days":19,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Data Science","role_family":"Data Science","seniority":"head","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Barcelona, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"AI Agents","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Model Context Protocol","optional":true}],"status":"live","first_seen_at":"2026-11-05T10:18:25Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-07T08:05:09Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site. Remote or travel flexibility is not available.\nWhy this role matters\nAstraZeneca is transforming research and development through better use of data, technology, and artificial intelligence. Within the Enterprise AI Unit, Enterprise Data Enablement develops the data capabilities needed to support AstraZeneca’s core business.\nThe R&D Data Office is building enterprise-level capabilities to make R&D data FAIR+T-findable, accessible, interoperable, reusable, and trustworthy. Reliable data and metadata pipelines are essential to achieving this ambition. They enable data to move efficiently from scientific and laboratory systems into trusted, discoverable, and reusable data products that can support analysis, automation, and AI-enabled discovery.\nAs an Associate Director, you will design, implement, deploy, and maintain Data pipelines that support the end-to-end management of data and metadata across R&D priority programmes . You will work on initiatives such as SafetyOmics and AI4Assay, helping ensure that internally generated data, including multi-omics data, is captured, processed, enriched, validated, traced, and made available for scientific use.\nThis is a hands-on individual contributor role. You will work closely with team leadership and technical colleagues to turn project needs into robust, production-ready pipeline solutions. Although the initial focus will be on specific projects, the pipelines you will develop should be modular, configurable, and reusable across a growing range of R&D use cases.\nWhat You Will Do\nData Pipeline Engineering\nDesign, build, test, deploy, and maintain robust pipelines for the ingestion, processing, transformation, validation, enrichment, and publication of R&D data and metadata.\nDeliver solutions supporting the end-to-end data lifecycle, from source-system integration through to FAIR data publication, scientific analysis, and downstream use.\nIntegrate data from internal systems, laboratory applications and instruments, APIs, databases, and file-based sources.\nBuild modular, configurable, and reusable pipelines that can support multiple projects and evolving scientific requirements.\nImplement appropriate data quality, validation, provenance, traceability, and error-handling controls.\nDevelop supporting APIs, services, workflow components, and data infrastructure where required.\nProduction Delivery & Engineering Excellence\nTake solutions from concept and prototype through production deployment, monitoring, troubleshooting, maintenance, and continuous improvement.\nApply strong software engineering practices, including version control, automated testing, CI/CD, observability, security, documentation, and controlled change management.\nOptimize pipelines for reliability, scalability, performance, maintainability, and cost efficiency.\nContribute hands-on code and help shape technical decisions and engineering approaches.\nCollaboration and Innovation\nPartner with scientists, bioinformaticians, data engineers, architects, data-management specialists, and other stakeholders to translate scientific and business requirements into practical technical solutions.\nSupport appropriate metadata structures, FAIR+T principles, data quality, and data-management standards.\nClearly communicate technical options, constraints, and solutions to both technical and non-technical audiences.\nCoordinate delivery with colleagues and outsourced service provider contractors where required.\nIdentify opportunities to use automation and AI-enabled approaches to improve data-management and engineering workflows.\nContribute to technical learning, knowledge sharing, and continuous improvement across the team.\nWhat We Are Looking For\nRequired Qualifications\nStrong experience designing, developing, deploying, and maintaining complex data pipelines or data-intensive software solutions.\nStrong hands-on Python programming skills and familiarity with other relevant programming languages.\nPractical experience with AWS services such as S3, Lambda, Glue, Step Functions, RDS, CloudWatch, and IAM, or equivalent cloud technologies.\nExperience with relational databases, APIs, data integration, workflow orchestration, and data-processing frameworks.\nProven experience taking pipelines from prototype into production, including deployment, monitoring, troubleshooting, lifecycle management, and continuous improvement.\nStrong understanding of data quality, validation, error handling, provenance, and traceability.\nAbility to design modular, reusable, configurable, and maintainable technical solutions.\nExperience applying software engineering practices including Git/version control, automated testing, CI/CD, observability, documentation, and secure development.\nPractical knowledge of automation and AI-assisted development for pipeline development, testing, documentation, monitoring, and maintenance.\nHands-on experience with coding agents, such as Claude Code or Codex, for pipeline prototyping, development, or testing.\nPreferred Qualifications\nUnderstanding of AI agents and agent orchestration, including agent frameworks, guardrails, Model Context Protocol (MCP), agent skills, and integration with data sources and pipelines.\nUnderstanding of security, governance, privacy, and responsible use in relation to AI agents and MCP-based tools.\nExperience with bioinformatics, multi-omics, genomics, laboratory data, or scientific data workflows.\nExperience within discovery, pre-clinical, translational, clinical, or other life-sciences research environments.\nExperience with non-relational databases, containers, Kubernetes, infrastructure-as-code, or hybrid cloud environments.\nExperience working with large datasets, scientific computing, or high-performance data processing.\nExposure to statistical, machine-learning, or AI techniques within data or scientific workflows.\nExperience working in regulated or quality-controlled environments.\nExperience mentoring colleagues or coordinating technical delivery through influence.\n#EAI\nDate Posted\n05-nov-2026Closing Date\n03-dic-2026AstraZeneca 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. 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