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# Data & AI Delivery Lead - Enterprise Asset Management (EAM)

- **Company:** Randstad
- **Location:** Washington, DC, United States
- **Experience:** Expert
- **Salary:** $156,000.0 - $176,800.0
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Data Analysis, Bioinformatics, Program Optimization, Data Architecture, Information Engineering, Data Governance, Machine Learning, Reliability Engineering, Search Technologies, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** September 4, 2026
- **Apply:** https://www.randstadusa.com/jobs/4/1345746/data-ai-delivery-lead-enterprise-asset-management-eam_washington/

## About the Role

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. Preferred Qualifications Direct experience with rail infrastructure, transit networks, or linear assets. Databricks Certified Data Engineer (Professional) or Databricks Certified Machine Learning (Associate/Professional).

## Description

job summary:
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.

location: Washington, Washington, D.C.
job type: Contract
salary: $75 - 85 per hour
work hours: 9am to 5pm
education: Bachelors

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.

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.

Preferred Qualifications

Direct experience with rail infrastructure, transit networks, or linear assets.

Databricks Certified Data Engineer (Professional) or Databricks Certified Machine Learning (Associate/Professional).

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

At Randstad Digital, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. If you require a reasonable accommodation to make your application or interview experience a great one, please contact HRsupport@randstadusa.com.

Pay offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. In addition, Randstad Digital offers a comprehensive benefits package, including: medical, prescription, dental, vision, AD&D, and life insurance offerings, short-term disability, and a 401K plan (all benefits are based on eligibility).

This posting is open for thirty (30) days.

,

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.


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