Support the mission of the Translational & Quantitative Science (TQS) data engineering function by creating high-quality data products that integrate preclinical and clinical translational datasets.
Lead the end-to-end lifecycle of TQS data products, including discovery, prototyping, architecture definition, implementation, validation, and deployment in partnership with enterprise data engineering team.
Architect modern data solutions using modern engineering patterns (e.g., lakehouse principles, modular pipelines, metadata-driven design) and develop fit-for-purpose data models, ETL/ELT workflows, and analytical infrastructure that meet the diverse needs of translational research.
Apply data product management principles to define features, requirements, and success metrics, ensuring data products deliver measurable scientific and operational value.
Partner with enterprise engineering teams to deliver scalable, automated, and maintainable infrastructure and deployment workflows, and drive data engineering excellence by enforcing best practices in code quality, CI/CD pipelines, testing, observability, and documentation within TQS’s data engineering organization.
Prototype new data or analytics approaches to evaluate emerging technologies, tools, or frameworks that could enhance TQS data capabilities.
Mentor team members and scientific partners on data engineering principles, modern data architecture.

Requirements

BS/MS/PhD in Computer Science, Bioinformatics, or a related field
8+ years of data engineering experience (Masters/PhD in a relevant field is a plus)
Proficiency in Python, R, and SQL, with proven experience building scalable, production-grade data pipelines and cloud-based architectures that support translational and clinical research workflows.
Strong experience with Databricks, including Spark (PySpark, SQL), Delta Lake, and Unity Catalog; familiarity with DBT for data transformations within the Databricks ecosystem is a plus.
Hands-on expertise with AWS services such as S3, Glue, Lambda, Step Functions, Data-sync, EMR, Redshift, and core IAM/networking concepts relevant to secure and compliant data engineering.
Experience implementing CI/CD pipelines (GitLab or similar), data testing frameworks, and infrastructure-as-code tooling (e.g., Terraform) to ensure reliable, automated, and scalable data operations.
Familiarity with common translational and clinical data types, such as flow cytometry, cytokine and biomarker assay outputs, genomics/transcriptomics (RNA-seq, DNA-seq), proteomics, and other multi-omics datasets.
Scientific knowledge in oncology is a plus.
Ability to support translational and clinical analysis through basic statistical methods and the development of dashboards or interactive tools (R Shiny, Streamlit, etc.) as well as business intelligence platforms such as Spotfire, Tableau, or Power BI to enable scientific decision-making.
Solid understanding of data governance, security, and compliance requirements in enterprise and research environments, including privacy considerations for clinical and biomarker data.
Experience working in Agile/Scrum environments, with the ability to manage sprint deliverables, collaborate effectively with cross-functional teams, and operate within iterative development cycles.
Knowledge of GxP validation practices and e-system management experience in biotech/pharma R&D environments coupled with a strong understanding of how data flows across research and development stages.

Benefits

401(k) Plan: 100% match on the first 6% of contributions
Health Benefits: Two medical plan options (including HDHP with HSA), dental, and vision insurance
Voluntary Plans: Critical illness, accident, and hospital indemnity insurance
Time Off: Paid vacation, sick leave, holidays, and 12 weeks of discretionary paid parental leave
Support Resources: Access to child and adult backup care, family support programs, financial wellness tools, and emotional well-being support
Additional Perks: Commuter benefits, tuition reimbursement, and a Lifestyle Spending Account for wellness and personal expenses

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonRSQLDatabricksSparkDelta LakeAWSCI/CDTerraformdata governance

Soft Skills

mentoringcollaborationcommunicationleadershipproblem-solvingagile methodologydata engineering excellencestatistical analysisproject managementcross-functional teamwork