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Data & AI Delivery Lead – Enterprise Asset Management (EAM)
Job Location:
Job Location:
Washington, DC - USA
Monthly Salary:
Not provided by the employer
Monthly Salary:
Posted:
5 September 2026 (10 hours ago)
Posted:
Application Deadline:
3 December 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Job Description
Randstad is seeking a high-caliberData & AI Delivery Lead - Enterprise Asset Management (EAM)to drive the end-to-end execution of advanced data analytics and AI/GenAI solutions for a major rail and transit client in the Washington DC area. Operating at the intersection of business strategy program delivery and hands-on technical execution this role serves as the primary technical leader owning the Databricks Lakehouse architecture to modernize infrastructure asset management condition monitoring and long-term capital planning. As a core delivery anchor within the Infrastructure EAM workstream you will lead cross-functional teams to transform traditional fixed-interval maintenance into predictive risk-based interventions that minimize operational risk and lower project costs.
Key Responsibilities
Technical Leadership & Solution Delivery:Oversee end-to-end delivery of analytics machine learning predictive modeling and GenAI use cases on the enterprise Databricks platform.
Scalable Data Pipeline Design:Build and optimize robust pipelines using Databricks Workflows and the Medallion Architecture to ingest process and curate complex sensor feeds inspection records maintenance histories and operational/financial datasets.
Predictive & Lifecycle Modeling:Guide the development of predictive health models failure probability algorithms and Remaining Useful Life (RUL) indicators alongside financial lifecycle cost models to support risk-based capital allocation.
Governance & Platform Optimization:Implement enterprise data governance lineage and security standards using Unity Catalog while evaluating and integrating modern Databricks features (e.g. Delta Live Tables MLflow Vector Search).
Stakeholder & Domain Alignment:Partner with engineering reliability and operations teams to deploy interactive dashboards and risk-scoring frameworks aligned with industry standards (e.g. ISO 55000) and regulatory requirements.
Program Execution:Bridge executive business strategy and technical execution during high-demand project phases serving as a dedicated expert backfill to drive productivity and maintain project momentum.
Required Qualifications
7 yearsof progressive experience in data engineering advanced data analytics or asset analytics roles.
3 yearsof project or program management experience leading complex enterprise data initiatives or asset management solutions.
Hands-on Databricks Command:Proven practical experience with the Databricks Lakehouse ecosystem including Medallion Architecture Unity Catalog and modern AI/ML tooling.
Domain Knowledge:Deep familiarity with reliability engineering condition monitoring predictive maintenance techniques or enterprise asset management concepts.
Location:Based in or willing to work with a client in the Washington DC area.
Preferred Qualifications
Direct experience with rail infrastructure transit networks or linear assets.
Databricks Certified Data Engineer (Professional) or Databricks Certified Machine Learning (Associate/Professional).
Randstad is seeking a high-caliberData & AI Delivery Lead - Enterprise Asset Management (EAM)to drive the end-to-end execution of advanced data analytics and AI/GenAI solutions for a major rail and transit client in the Washington DC area. Operating at the intersection of business strategy program delivery and hands-on technical execution this role serves as the primary technical leader owning the Databricks Lakehouse architecture to modernize infrastructure asset management condition monitoring and long-term capital planning. As a core delivery anchor within the Infrastructure EAM workstream you will lead cross-functional teams to transform traditional fixed-interval maintenance into predictive risk-based interventions that minimize operational risk and lower project costs.
Key Responsibilities
Technical Leadership & Solution Delivery:Oversee end-to-end delivery of analytics machine learning predictive modeling and GenAI use cases on the enterprise Databricks platform.
Scalable Data Pipeline Design:Build and optimize robust pipelines using Databricks Workflows and the Medallion Architecture to ingest process and curate complex sensor feeds inspection records maintenance histories and operational/financial datasets.
Predictive & Lifecycle Modeling:Guide the development of predictive health models failure probability algorithms and Remaining Useful Life (RUL) indicators alongside financial lifecycle cost models to support risk-based capital allocation.
Governance & Platform Optimization:Implement enterprise data governance lineage and security standards using Unity Catalog while evaluating and integrating modern Databricks features (e.g. Delta Live Tables MLflow Vector Search).
Stakeholder & Domain Alignment:Partner with engineering reliability and operations teams to deploy interactive dashboards and risk-scoring frameworks aligned with industry standards (e.g. ISO 55000) and regulatory requirements.
Program Execution:Bridge executive business strategy and technical execution during high-demand project phases serving as a dedicated expert backfill to drive productivity and maintain project momentum.
Required Qualifications
7 yearsof progressive experience in data engineering advanced data analytics or asset analytics roles.
3 yearsof project or program management experience leading complex enterprise data initiatives or asset management solutions.
Hands-on Databricks Command:Proven practical experience with the Databricks Lakehouse ecosystem including Medallion Architecture Unity Catalog and modern AI/ML tooling.
Domain Knowledge:Deep familiarity with reliability engineering condition monitoring predictive maintenance techniques or enterprise asset management concepts.
Location:Based in or willing to work with a client in the Washington DC area.
Preferred Qualifications
Direct experience with rail infrastructure transit networks or linear assets.
Databricks Certified Data Engineer (Professional) or Databricks Certified Machine Learning (Associate/Professional).
Required Skills:
MODELING
