Scientist, Data Engineering Full-time

Scientist Position in NGS & Bioinformatics Group

Are you ready to make a significant impact in the field of cell therapy? In AstraZeneca's Cell Therapy division, we follow the science to explore and innovate. We are working towards treating, preventing, modifying, and even curing some of the world's most complex diseases. We have the potential to grow our pipeline and positively impact the lives of patients around the world. We are committed to making a difference and have built our business around our passion for science. Now we are fusing data and technology with the latest scientific innovations to achieve the next wave of breakthroughs.

AstraZeneca is looking for a Scientist to join the NGS & Bioinformatics group within Cell Therapy Translational and Safety Sciences (CT-TSS). In this data engineering-focused role, you will build and maintain the data architecture, workflows, and computing environments that turn single-cell RNAseq and other NGS data into reliable, well-annotated, AI-ready datasets across our cell therapy pipeline (CAR/TCR/in vivo). Your expertise will be crucial in enabling translational and safety science insights and advancing our programs for patients.

Accountabilities

• Design, build, and maintain scalable data architecture and data workflows that support single-cell RNAseq and other NGS data generated across Cell Therapy clinical and translational programs.
• Develop, automate, and optimize reproducible data processing pipelines (e.g., Cell Ranger, Nextflow/Snakemake-based workflows) from raw sequencing output through to analysis-ready datasets.
• Manage and administer NGS data across Linux servers, high-performance computing clusters, and cloud computing environments, ensuring efficient storage, compute scaling, and data lifecycle management.
• Establish data standards, metadata frameworks, and data quality checks aligned with FAIR principles to ensure NGS datasets are traceable, harmonized, and reusable across programs.
• Lead metadata annotation efforts for NGS and single-cell datasets, including sample, clinical, assay, and processing metadata, using controlled vocabularies and ontologies to ensure consistent, accurate, and searchable annotation.
• Ensure data is AI-ready by curating, harmonizing, and structuring NGS and single-cell RNAseq datasets (with complete metadata, versioned lineage, and quality controls) so they can be readily used for machine learning and AI model development.
• Partner with bioinformaticians, translational and safety scientists, and wet-lab teams to understand analytical needs and translate them into robust data engineering solutions.
• Implement version control, containerization, and documentation best practices, and contribute to a shared code repository and team standards for bioinformatic and data engineering work.
• Coordinate with IT, data engineering, and external vendors/CROs on data transfer, ingestion, integrity (e.g., checksum/manifest verification), and access controls for sequencing data.
• Monitor pipeline performance and troubleshoot failures, data quality issues, and infrastructure bottlenecks to maintain reliable, timely data delivery.
• Support integration of single-cell RNAseq data with clinical, biomarker, and other multi-omic datasets to enable translational and safety science insights.
• Document methodologies, workflows, and data lineage, and communicate technical concepts clearly through reports, presentations, and cross-functional discussions.
• Stay current with advances in data engineering, cloud technologies, and single-cell/NGS data tools, and apply them to continuously improve team capabilities.

Essential Skills/Experience

• Bachelor's degree in bioinformatics, computational biology, computer science, data science, bioengineering, or a related field, with 4+ years of relevant experience.
• Demonstrated experience in data engineering, including designing data architecture and establishing automated, reproducible data workflows for scientific or biomedical data.
• Expertise working across data environments, including Linux server administration and command-line proficiency, high-performance computing clusters, and cloud computing platforms (e.g., AWS, Azure, or GCP).
• Required familiarity with single-cell RNAseq workflows, including data generation, raw data processing (e.g., Cell Ranger), and typical QC outputs.
• Proficiency in programming and scripting languages such as Python, R, and Bash/shell, with experience building and maintaining production-quality pipelines.
• Experience with workflow management and orchestration tools (e.g., Nextflow, Snakemake, Airflow) and with containerization (e.g., Docker, Singularity).
• Experience with version control (Git), code documentation, and software development best practices.
• Working knowledge of databases and data storage solutions (SQL and/or NoSQL, object storage), data modeling, and metadata management.
• Experience with metadata annotation and management, including standardized schemas, controlled vocabularies/ontologies, and curation of sample and clinical metadata for genomic datasets.
• Understanding of what makes data AI-ready (e.g., clean, well-annotated, harmonized, versioned, and machine-readable datasets) and experience preparing data for machine learning workflows.
• Understanding of data integrity, security, and access-control practices for sensitive clinical and genomic data.
• Familiarity with FAIR data principles and harmonized data practices for genomics is a plus.
• Background in cell therapy, immunology, or tumor biology, or experience supporting translational or clinical research datasets, is a plus.
• Team-oriented mindset with strong written and verbal communication skills, and the ability to work independently and collaboratively with cross-functional stakeholders.
• Ability to prioritize multiple projects and adjust workload based on changing priorities in a fast-paced environment.