Data Engineer / Architect

• Design and develop scalable data pipelines using dbt Cloud, Databricks and Apache Airflow to support enterprise analytics and reporting
• Build and optimize Delta Lake-based data models to enable analytics-ready datasets
• Implement advanced data modeling techniques including star schema, fact/dimension design, and SCD Type 1 & Type 2
• Develop modular, reusable, and testable SQL-based transformations using DBT models, macros, and packages
• Design and manage incremental data loading strategies, ensuring efficient processing of large-scale datasets
• Leverage Databricks SQL, Spark, and Delta Lake capabilities for high-performance data processing and optimization
• Implement robust data quality checks and testing frameworks using DBT tests (e.g., not null, unique, referential integrity)
• Collaborate with cross-functional teams including data engineers, data scientists, and BI teams to deliver business-driven data solutions
• Integrate DBT pipelines with CI/CD workflows using Git-based version control and orchestrate jobs via Databricks Workflows or external schedulers
• Ensure adherence to data governance, security, and compliance standards, leveraging tools like Unity Catalog and enterprise policies.
• Orchestrate end-to-end workflows using Airflow DAGs, ensuring dependency management, scheduling, retries, and fault tolerance

Technical Expertise

• AWS & Cloud Architecture: Expert-level experience with AWS services (S3, RDS, Bedrock agents), PostgreSQL, and cloud-based data governance
• Advanced Analytics: Regression analysis, time-series forecasting, multivariate analysis, and classification models
• MLOps & Deployment: Design and maintain model deployment, monitoring, and automated retraining pipelines
• Simulation & Forecasting: Agent-based simulation for trial enrollment forecasting and scenario planning

Data & Analytics Capabilities

• Feature Engineering: Extract insights from site performance, historical enrollment, and competitive landscape data
• Model Evaluation: Build evaluation frameworks (AUC, precision/recall) and optimize model granularity across disease/geography
• Enterprise Data Integration: Merge internal (CTMS, performance data) and external sources (Citeline, epidemiological data)
• Master Data Management: Create Golden ID datasets with data quality monitoring and continuous refresh capabilities

• 5+ years in pharmaceutical/clinical trial analytics
• Focus on site selection and non-enrollment prediction
• Proven track record with clinical operations data systems

• $108,000-120,000 per year