Data & AI Delivery Lead || Washington, DC || Local Candidates Only

We are 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.

This role operates at the intersection of business strategy, program delivery, and hands-on technical execution. The successful candidate will serve as the primary technical leader responsible for Databricks Lakehouse architecture supporting infrastructure asset management, condition monitoring, and long-term capital planning.

Key Responsibilities

Lead end-to-end delivery of analytics, machine learning, predictive modeling, and GenAI use cases on the enterprise Databricks platform.

Design, build, and optimize scalable data pipelines using Databricks Workflows and Medallion Architecture.

Ingest, process, and curate complex datasets including sensor feeds, inspection records, maintenance histories, and operational/financial data.

Guide development of predictive asset health models, failure probability algorithms, and Remaining Useful Life (RUL) indicators.

Support financial lifecycle cost modeling and risk-based capital allocation.

Implement enterprise data governance, lineage, and security standards using Unity Catalog.

Evaluate and integrate modern Databricks capabilities including Delta Live Tables, MLflow, and Vector Search.

Partner with engineering, reliability, and operations teams to deliver interactive dashboards and risk-scoring frameworks.

Align solutions with industry standards such as ISO 55000 and applicable regulatory requirements.

Lead cross-functional teams through complex enterprise data and AI initiatives.

Bridge executive business strategy and technical execution during critical project phases.

Drive productivity, resolve technical challenges, and maintain project momentum.

Required Qualifications

7+ years of progressive experience in data engineering, advanced data analytics, or asset analytics.

3+ years of project or program management experience leading complex enterprise data initiatives or asset management solutions.

Strong hands-on experience with the Databricks Lakehouse ecosystem.

Proven experience with:

Databricks Medallion Architecture

Unity Catalog

Databricks Workflows

AI/ML tooling

Data pipelines and analytics

Deep knowledge of:

Reliability engineering

Condition monitoring

Predictive maintenance

Enterprise Asset Management (EAM)

Strong technical leadership and program delivery experience.

Excellent communication and stakeholder management skills.

Preferred Qualifications

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

Databricks Certified Data Engineer – Professional certification.

Databricks Certified Machine Learning – Associate/Professional certification.

Experience with predictive asset health, failure prediction, or Remaining Useful Life (RUL) modeling.

Experience implementing enterprise data governance and security frameworks.

Experience working with engineering, reliability, operations, and asset management stakeholders.