Data & AI Delivery Lead – Enterprise Asset Management (EAM)

Job Summary (List Format):

- Serve as the primary technical leader for advanced data, analytics, and AI/GenAI solution delivery for a major rail and transit client.
- Own and architect the Databricks Lakehouse platform to modernize infrastructure asset management and enable predictive maintenance.
- Oversee end-to-end delivery of analytics, machine learning, predictive modeling, and GenAI use cases.
- Design, build, and optimize scalable data pipelines using Databricks Workflows and Medallion Architecture for diverse asset data sources.
- Guide development of predictive health models, failure probability algorithms, and Remaining Useful Life (RUL) indicators.
- Develop financial lifecycle cost models to support risk-based capital planning and asset allocation.
- Implement enterprise data governance, lineage, and security standards using Unity Catalog and other Databricks features.
- Collaborate with engineering, reliability, and operations teams to deploy dashboards and risk-scoring frameworks aligned with industry and regulatory standards (e.g., ISO 55000).
- Bridge business strategy and technical execution during critical project phases to maintain momentum and drive results.
- Lead cross-functional teams within the Infrastructure EAM workstream to transform maintenance strategies.
- Requirements include 7+ years in data engineering/analytics, 3+ years in program/project management, hands-on Databricks experience, and strong domain knowledge in asset management.
- Preferred: Experience with rail infrastructure or transit networks and Databricks certifications.
- Position is based in or requires willingness to work with a client in Washington, DC.