Scientist, Data Engineering

• 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.

• 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.

The annual base pay for this position ranges from $90,024 - $135,036 USD. Hourly and salaried non-exempt employees will also be paid overtime pay when working qualifying overtime hours. Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition, our positions offer a short-term incentive bonus opportunity; eligibility to participate in our equity-based long-term incentive program (salaried roles), to receive a retirement contribution (hourly roles), and commission payment eligibility (sales roles). Benefits offered included a qualified retirement program [401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans. Additional details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base pay (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.