Principal Data & AI Architect - Operations & Asset Analytics

Randstad is seeking a high-caliber Principal Data & AI Architect Operations & Asset Analytics to lead the end-to-end delivery of enterprise data, advanced analytics, and AI/GenAI solutions within the rail and transportation operations domain for a major Washington, DC-based client. Operating at the intersection of business strategy and hands-on technical execution, this role will serve as the technical authority owning the Databricks Lakehouse architecture to modernize infrastructure asset management (EAM), condition monitoring, and long-term capital planning. The ideal candidate will blend strategic program leadership with deep expertise in asset analytics-transforming traditional fixed-interval maintenance into predictive, risk-based interventions while supporting critical infrastructure projects. Key Responsibilities

• Architect & Deliver Data/AI Solutions: Lead the technical vision and end-to-end delivery of analytics, machine learning, predictive modeling, and GenAI use cases on the enterprise Databricks platform.
• Build Scalable Data Pipelines: Design, optimize, and oversee production data pipelines utilizing Databricks Workflows and the Medallion Architecture to ingest and curate complex sensor feeds, inspection records, maintenance histories, and operational/financial datasets.
• Advanced Predictive & Lifecycle Modeling: Guide the design and deployment of predictive health models, failure probability algorithms, and Remaining Useful Life (RUL) indicators, alongside financial lifecycle cost models for risk-based capital allocation.
• Governance & Platform Optimization: Implement enterprise-grade data governance, lineage, and security frameworks using Unity Catalog, while continuously evaluating and integrating modern Databricks features (e.g., Delta Live Tables, MLflow, Vector Search).
• Cross-Functional & Business Stakeholder Alignment: Partner closely with engineering, reliability, and asset management teams to deploy interactive dashboards and risk-scoring frameworks aligned with industry standards (e.g., ISO 55000) and regulatory requirements.
• Workstream Program Leadership: Serve as a core technical anchor within the Infrastructure EAM workstream, bridging executive strategy and technical execution during high-demand project phases to reduce operational risk and project costs.

• 7+ years of progressive experience in data architecture, data engineering, or advanced asset analytics roles.
• 3+ years of program or project leadership experience driving complex enterprise data solutions or asset management initiatives.
• Proven Hands-On Databricks Expertise: Strong practical command of the Databricks Lakehouse ecosystem, Medallion Architecture, Unity Catalog, and modern ML/AI tooling.
• Domain Knowledge: Solid understanding of reliability engineering, condition-based monitoring, predictive maintenance techniques, or enterprise asset management (EAM) concepts.
• Preferred Experience: Direct experience working with rail infrastructure, transit networks, or linear assets.
• Preferred Certifications: Databricks Certified Data Engineer Professional or Databricks Certified Machine Learning Professional/Associate.

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Background Check : No

Drug Screen : No