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
Experience working in Agile/Scrum environments
Familiarity with DBT for data transformations within the Databricks ecosystem is a plus
Strong experience with Databricks, including Spark (PySpark, SQL), Delta Lake, and Unity Catalog
Proficiency in Python, R, and SQL
Knowledge of GxP validation practices and e-system management and deployment experience in biotech/pharma R&D environments
BS/MS/PhD in Computer Science, Bioinformatics, or a related field with 8+ years of analytic engineering experience
Scientific knowledge in oncology is a plus
Experience implementing CI/CD pipelines (GitLab or similar), data testing frameworks, and infrastructure-as-code tooling (e.g., Terraform)
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 analytic engineering
Solid understanding of data governance, security, and compliance requirements in enterprise and research environments, including privacy considerations for clinical and biomarker data
Proven experience building scalable, production-grade data pipelines and cloud-based architectures that support translational and clinical research workflows
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

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