Scientist, Data Engineering

Description

The company works on cell therapy, aiming to treat, prevent, modify and eventually cure some serious diseases, and is combining data and technology with new science to drive the next wave of progress.

The team is the NGS and Bioinformatics group inside Translational and Safety Sciences for Cell Therapy. This is a data engineering role that builds and runs the data architecture, workflows and computing environments that turn single-cell RNAseq and other NGS data into reliable, well-annotated, AI-ready datasets across the CAR, TCR and in vivo programs.

Your work will help the team produce translational and safety insights and move programs forward for patients.

Responsibilities

• Design, build and maintain scalable data architecture and workflows for single-cell RNAseq and other NGS data from clinical and translational cell therapy programs.
• Automate and tune reproducible processing pipelines, such as Cell Ranger and Nextflow or Snakemake workflows, from raw sequencing output to analysis-ready data.
• Administer NGS data on Linux servers, HPC clusters and cloud platforms, managing storage, compute scaling and the data lifecycle.
• Set data standards, metadata frameworks and quality checks based on FAIR principles so NGS datasets are traceable, harmonized and reusable.
• Lead metadata annotation for NGS and single-cell datasets, covering sample, clinical, assay and processing metadata, using controlled vocabularies and ontologies.
• Curate and structure NGS and single-cell RNAseq data with complete metadata, versioned lineage and quality controls so it is ready for machine learning and AI model development.
• Work with bioinformaticians, translational and safety scientists and wet-lab teams to turn their analysis needs into sound data engineering solutions.
• Apply version control, containerization and documentation practices, and contribute to the shared code repository and team standards.
• Coordinate with IT, data engineering and external vendors or CROs on sequencing data transfer, ingestion, integrity checks such as checksum and manifest verification, and access controls.
• Monitor pipeline performance and fix failures, data quality problems and infrastructure bottlenecks to keep data delivery reliable and on time.
• Help integrate single-cell RNAseq data with clinical, biomarker and other multi-omic datasets.
• Document methods, workflows and data lineage, and explain technical topics clearly in reports, presentations and cross-functional discussions.
• Keep up with developments in data engineering, cloud technology and single-cell and NGS tools, and apply them to improve the team.

Requirements

• A bachelor's degree in bioinformatics, computational biology, computer science, data science, bioengineering or a related field, plus 4+ years of relevant experience.
• Proven data engineering experience, including designing data architecture and building automated, reproducible workflows for scientific or biomedical data.
• Hands-on skill with Linux server administration, the command line, HPC clusters and cloud platforms such as AWS, Azure or GCP.
• Required: familiarity with single-cell RNAseq workflows, including data generation, raw processing such as Cell Ranger, and typical QC outputs.
• Proficiency in Python, R and Bash or shell scripting, with experience building and maintaining production-quality pipelines.
• Experience with workflow orchestration tools such as Nextflow, Snakemake or Airflow, and with containers such as Docker or Singularity.
• Experience with Git, code documentation and software development best practices.
• Working knowledge of SQL and/or NoSQL databases, object storage, data modeling and metadata management.
• Experience annotating and managing metadata with standard schemas, controlled vocabularies or ontologies, and curating sample and clinical metadata for genomic data.
• Understanding of what makes data AI-ready, such as clean, well-annotated, harmonized, versioned and machine-readable data, and experience preparing data for machine learning.
• Understanding of data integrity, security and access control for sensitive clinical and genomic data.
• Plus: familiarity with FAIR principles and harmonized data practices for genomics.
• Plus: background in cell therapy, immunology or tumor biology, or experience supporting translational or clinical research datasets.
• A team-oriented approach, strong written and verbal communication, and the ability to work independently and with cross-functional stakeholders.
• Ability to juggle several projects and adjust priorities quickly in a fast-moving environment.

Conditions

Annual base pay ranges from $90,024 to $135,036 USD. Actual pay depends on factors such as market location, job-related knowledge, skills and experience.

Hourly and salaried non-exempt employees receive overtime pay for qualifying overtime hours.

This is an at-will position, and the company may change base pay or other discretionary pay and compensation programs at any time, based on individual or team performance and market conditions.

The company is committed to equal employment opportunity and considers all qualified applicants regardless of protected characteristics. Accommodation requests can be made in the application form.

Date posted: 28-Oct-2026. Closing date: 10-Nov-2026.

Benefits

• Short-term incentive bonus opportunity.
• Equity-based long-term incentive program for salaried roles, and a retirement contribution for hourly roles.
• Qualified retirement plan (401(k)).
• Paid vacation, holidays and leave.
• Health benefits, including medical, prescription drug, dental and vision coverage, under the terms of the applicable plans. Plan details are provided on receipt of an offer.