Data & AI Solutions Architect

The Data & AI Solutions Delivery Lead will serve as the technical lead owning end-to-end delivery of data, analytics, AI and business intelligence solutions in the Operations domain. Using the Databricks Lakehouse Platform as the primary data and analytics environment, this role blends program leadership with hands on technical expertise to enable data driven decision making for capital planning, train operations, condition-based and predictive maintenance and long-term asset performance.

Required Qualifications

• Min 7+ years of experience in data analytics, data engineering, or asset analytics roles.
• Min 3+ years of program or project management experience delivering complex data solutions or asset management initiatives.
• Hands on experience with Databricks or other data and intelligence platforms.

Preferred Skills & Experience

• Experience with rail infrastructure/linear assets.
• Knowledge of predictive maintenance techniques.
• Strong understanding of asset management, condition monitoring, or reliability engineering concepts.
• Certifications such as Databricks Data Engineer or ML Associate/Professional.

Technical Leadership & Solution Delivery (Databricks & Asset Analytics)

• Oversee the delivery of analytics, reporting, machine learning, predictive modeling, and GenAI use cases on the enterprise Databricks platform.
• Build and optimize data pipelines using Databricks Workflows and Medallion Architecture to ingest, process and curate sensor data, inspection results, maintenance history, train operations and financial data.
• Guide the development of predictive models for asset health, failure probability, and remaining useful life (RUL).
• Implement best practices for data governance, lineage, and security using Unity Catalog.
• Evaluate and integrate new Databricks capabilities (e.g., MLflow, Delta Live Tables, vector search) to enhance asset analytics.

Asset Investment Planning & Condition Monitoring

• Architect analytical frameworks and lifecycle cost models to guide risk-based prioritization and long-term capital planning.
• Partner with engineering and asset teams to build interactive dashboards and risk-scoring models that turn condition data into actionable maintenance insights.
• Work with business partners to ensure alignment of data products and solutions with regulatory, compliance, and industry standards for asset management (e.g., ISO 55000).