• Be a hands-on technical visionary leader for the data engineering team responsible for solutioning, designing, implementing, reviewing, and deploying core functionality to support our data warehouse, data pipelines, data sharing, data integration and BI tools strategy. • Manage, scale, and mentor the data engineering team along with leading the recruitment efforts to attract and retain top data engineering talent. • Collaborate with stakeholders to prioritize top business initiatives and develop corresponding solutions and a strategic roadmap. • Identify and evaluate emerging data engineering technologies and trends, and determine their potential application within the organization. • Manage resource allocation and bi-weekly sprint planning. • Advocate for and drive the adoption of best practices in data engineering development, deployment, and data governance. • Work closely with the data science, analytics, and operations teams to develop user-friendly data solutions that comply with established data governance guidelines.

• Support project teams in the design, development, and delivery of RWD solutions across multiple databases. • Manage the inflow of diverse healthcare data sources and ensure data quality. • Develop technical specifications and transform data into a common data model. • Publish validated datasets for enterprise-wide use. • Provide reports, presentations, and technical explanations to epidemiologists, analysts, and programmers. • Communicate with data vendors and ensure accountability for data quality. • Help ensure quality standards and evaluate new methodologies. • Perform SQL querying, transformation, and quality control. • Apply SAS, Python, and programming tools to support analytical workflows. • Work with real-world healthcare datasets (Optum, PharMetrics, Flatiron, registry data). • Support OMOP data model conversions. • Leverage AI tools such as Databricks Genie and MS Copilot. • Work independently or as part of a team.

• Design and iterate data extraction and transformation pipelines that convert unstructured leasing documents into structured JSON stores. • Write and optimize LLM API calls and prompts to extract and interpret text data at scale. • Orchestrate AI-driven workflows integrating multiple LLM models to handle diverse document types and edge cases. • Build and maintain ELT workflows in Python, managing data flows between cloud storage and relational databases. • Develop data quality and validation frameworks to ensure structured outputs are accurate and production-ready. • Implement monitoring, alerting, and automated quality checks across extraction pipelines. • Collaborate with product and engineering teams to define and evolve data schemas. • Own the pipeline end-to-end — from raw ingestion to validated structured output.

• Build and lead Battle Road’s data platform from the ground up. • Architect our AWS-based data warehouse and ingestion pipelines • Transform high-volume simulation outputs into clean, trusted datasets • Establish schema standards and data contracts with engineering • Enable scenario replay, comparison, and traceability • Support analytics and AI workflows across the platform • Hire and develop a high-performing data engineering team • Within your first year, you will stand up a scalable warehouse, integrate core simulation feeds, and deliver reliable insight into product usage and simulation behavior. • You will be modeling a dynamic world state, not just logs.