Job Description
Randstad is seeking a high-caliber Data & 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+ years of progressive experience in data engineering, advanced data analytics, or asset analytics roles.
3+ years of 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).
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 91175618
- Position Id: OOJ - 1029-33-1788530217
- Posted 3 days ago
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