Are you ready to engineer the data backbone that moves rare disease development labs from manual processes to automated, AI-ready science? Based in Dublin, you will help connect instruments, robotics, and scientific systems into a coherent, governed data ecosystem that speeds decisions, strengthens data integrity, and unlocks scalable analytics and machine learning.
You will join a fast-moving, cross-functional engineering group partnering closely with laboratory practitioners. Your work will turn raw lab outputs into trusted, reusable data products in a modern data mesh on Databricks and AWS. What could you build when scientists, roboticists, and data engineers solve problems together at the bench and in the cloud?
Lab-to-Cloud Connectivity: Engineer resilient connectivity from analytical instruments, lab equipment, and robotics across diverse vendors and data types, reducing manual handling and data silos.
End-to-End Pipelines: Design and deliver automated ingestion, validation, transformation, and loading into standardized, analysis-ready datasets that scientists trust.
Data Mesh Delivery: Curate and publish domain data products into a lake house-based mesh (Databricks on AWS) that enables self-service analytics and ML across teams.
ELN-to-Mesh Automation: Partner with scientific and digital stakeholders to implement agentic/LLM-assisted extraction from ELNs into governed data products with clear provenance.
Quality, Security, and Compliance by Design: Embed auditability, data integrity controls, and role-based access throughout pipelines to meet regulatory expectations.
Monitoring and Reliability: Implement logging, alerting, lineage, retries, and runbooks that improve reliability and turnaround times for laboratory workflows.
Standardized Schemas: Establish common shaping and schemas for key lab data types (chromatographic, spectroscopic, assay, automation run outputs) to accelerate reuse.
Scientist-Ready Access: Create APIs, datasets, and curated views that are discoverable and documented so scientists can find, trust, and reuse data without copy/paste.
Cross-Functional Delivery: Translate needs with scientists, automation/robotics, informatics, IT, and business partners into shipped, adopted capabilities that scale from a single instrument to entire labs.
Scalable Patterns and Impact: Evolve patterns and frameworks that shorten time-to-value, enable advanced automation and AI/ML, and form the foundation for future lab innovation.
Education: BS in Computer Science, Data Engineering, Software Engineering, Bioinformatics, Statistics, Engineering, or related; MS a plus.
Experience: 5+ years in data/software engineering delivering production grade pipelines (life sciences lab environment strongly valued but not required).
Core technical skills: strong Python and SQL; data pipeline orchestration patterns; data modelling for analytics/AI use cases.
Cloud & platform experience: hands on AWS (IAM, S3, EC2/EKS/Glue, monitoring) and Databricks/Delta Lake.
Integration mindset: connect heterogeneous sources (files, vendor exports, REST APIs); build resilient ingestion for real-world lab data.
Collaboration & communication: translate between scientists and engineers; define clear requirements; deliver in a fastmoving, high ownership environment.
Travel requirement: Ability to travel to New Haven, CT ~4x/year, 2–4 weeks per trip, to work face to face with laboratory practitioners and gain hands on familiarity with equipment and robotics.
Certifications:
- PMP (Project Management Professional) or PgMP (Program Management Professional).
And/or
- Agile Certifications such as Certified ScrumMaster (CSM) or Agile Certified Practitioner (PMI-ACP).
- Strong understanding of supply chain processes, project and program management methodologies, tools, and techniques, including Agile methodologies.
Here, your engineering will touch real patient journeys in rare disease, blending the energy and autonomy of a nimble biotech with the reach and investment of a global biopharma. You will work with cutting-edge platforms on Databricks and AWS, with unexpected teams in the same room unleashing bold thinking—scientists, roboticists, and data engineers aligning on outcomes. We value kindness alongside ambition, so you can take ownership, grow your craft, and build capabilities that matter to patients and colleagues today while laying the groundwork for tomorrow’s automation and AI.
If you want your pipelines to accelerate real decisions and power AI-ready science, step forward and help build the data backbone that moves rare disease labs faster and farther!
Alexion, AZ RDU may consider visa sponsorship under the Critical Skills route, where required, provided the candidate’s background and experience align with the requirements of the role.
Date Posted
17-Jul-2026
Closing Date
30-Jul-2026
Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.
