Bristol Myers SquibbPosted 1 month ago

Data Engineer, Clinical Operations

Bristol Myers SquibbPosted 1 month ago

$87,810–$106,399 year

HybridPrinceton, New Jersey, United States

Princeton, New Jersey, United StatesHybridFull Time$87,810–$106,399 yearEnterprise

Full TimeEnterprise

Job Summary

Design, build, and maintain scalable, production-grade data pipelines and platform components supporting Cross Study Operations and Specimen Management product lines, including cross-trial data aggregation, specimen tracking, and biobanking workflows. Collaborate with BI&T partners, clinical study teams, and domain experts to support effective adoption of the Data Platform, ensuring robustness, interoperability, and scalability. Develop and operationalize GenAI-powered and NLP-driven applications using Databricks Mosaic AI and MLflow to deliver efficiency gains, specimen traceability improvements, and compliance automation. Leverage Databricks, Delta Lake, and semantic technologies to enforce data governance, manage metadata, and enable self-service data discovery solutions for large, complex datasets. Serve as a go-to technical expert, providing guidance on technical best practices and fostering continuous learning within the data engineering community.

Required Qualifications

5+ years of hands-on experience in Data Engineering, Analytics, and AI/ML
Hands-on expertise with Databricks, including Delta Lake, Unity Catalog, Databricks Workflows, Mosaic AI, and MLflow
Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional)
Demonstrated expertise in cloud-native data platforms, ETL/ELT pipeline design, data modeling, and semantic analytics for large-scale, complex datasets
Hands-on DevOps experience
Strong proficiency in Python, SQL, Spark (including PySpark on Databricks), and GenAI frameworks
Hands-on experience with LLM architectures, RAG, prompt engineering, and agentic frameworks
Proficiency in creating and maintaining optimal data pipeline architecture for large, complex datasets, including semantic modeling within a domain
Demonstrated experience delivering production-grade GenAI applications, predictive models, and self-service analytics tools supporting critical clinical or research business functions
Working knowledge of LLM and GenAI-driven approaches, including RAG, Chain-of-Thought, fine-tuning, vectorization, agentic frameworks, and prompt engineering techniques
Strong stakeholder engagement and communication skills
Ability to clearly articulate technical concepts to non-technical audiences
Commitment to engineering excellence, documentation, and process improvement
Functional knowledge of Life Sciences R&D and clinical trial operations

Desired Qualifications

Experience in a life sciences, clinical trial operations, or specimen/biobanking workflows
Experience with Generative AI (GenAI), Databricks, and semantic technologies
Experience with Databricks Mosaic AI and MLflow
Experience with RAG, fine-tuning, and vector embeddings
Experience with cloud orchestration and containerization
Experience with Databricks Delta Lake, caching, and partitioning
Experience with Databricks Unity Catalog
Experience with semantic modeling
Experience with interoperability standards for large, complex cross-study and specimen datasets
Experience with data governance, metadata management, and end-to-end data lineage
Experience with self-service data discovery solutions
Experience with data product development, standardization, testing, and access governance
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with data assets
Experience with junior analysts, interns, and vendor resources
Experience with technical best practices
Experience with business alignment
Experience with continuous learning, engineering excellence, and knowledge sharing
Experience with cross-functional teams
Experience with BI&T partners
Experience with business analysts
Experience with data engineers
Experience with Clinical Operations specialists
Experience with Specimen Management professionals
Experience with Cross-Study Operations leads
Experience with domain experts
Experience with data-driven initiatives
Experience with the BMS data ecosystem
Experience with data product lifecycle
Experience with ingesting, storing, processing, governing, and interacting with data
Experience with high-quality, scalable data solutions
Experience with innovative, reliable, secure, and easy-to-use data ecosystems
Experience with cross-study operations and biospecimen workflows
Experience with clinical data challenges
Experience with cloud platforms
Experience with Generative AI
Experience with data engineering
Experience with cross-study operations
Experience with biospecimen workflows
Experience with data product teams
Experience with data operations
Experience with effective adoption of our Data Platform
Experience with scalable, production-grade data pipelines and platform components
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with robustness, interoperability with consumer applications, and scalability
Experience with cloud-native parallel processing, Databricks Delta Lake, caching, and partitioning
Experience with ETL/ELT pipelines and data models
Experience with Databricks, Delta Lake, cloud-native tools, semantic modeling, and interoperability standards
Experience with large, complex cross-study and specimen datasets in life sciences
Experience with hands-on technical solutions for data product development, standardization, testing, lineage, meeting latency requirements, and ensuring access governance
Experience with cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness
Experience with guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment
Experience with a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community
Experience with cross-study operations and specimen management
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with access governance across cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness
Experience with guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment
Experience with a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community
Experience with cross-study operations and specimen management
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with access governance across cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness
Experience with guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment
Experience with a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community
Experience with cross-study operations and specimen management
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with access governance across cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness
Experience with guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment
Experience with a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community
Experience with cross-study operations and specimen management
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with access governance across cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness
Experience with guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment
Experience with a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community
Experience with cross-study operations and specimen management
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with access governance across cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness
Experience with guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment
Experience with a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community
Experience with cross-study operations and specimen management
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with access governance across cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness
Experience with guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment
Experience with a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community
Experience with cross-study operations and specimen management
Experience with cross-trial data aggregation, specimen tracking, and biobanking workflows
Experience with cross-study clinical R&D programs
Experience with consumer applications
Experience with latency requirements
Experience with access governance across cross-study and specimen management data assets
Experience with self-service data discovery solutions
Experience with findability, accessibility, and reusability of cross-study operational and specimen data assets
Experience with thorough documentation of processes, data structures, and technical solutions
Experience with clear technical recommendations and executing solutions effectively across the enterprise
Experience with GenAI-powered and NLP-driven applications
Experience with efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation
Experience with cloud-based GenAI and LLM-powered applications
Experience with data product owners, engineers, and data scientists
Experience with RAG, fine-tuning, and vector embeddings
Experience with high-quality data discovery and consumption features for cross-study and specimen management workflows
Experience with Databricks Mosaic AI and MLflow
Experience with machine learning and GenAI models at scale within cross-study and specimen management contexts
Experience with technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization
Experience with emerging best practices to optimize platform performance, scalability, and cost-effectiveness

Hiring someone like this?

Get your role in front of qualified candidates on Sorce.

Get started