Data Information Architect

Must have skill - Databricks Workflows and Medallion Architecture.

What this person will do

This is a hands-on Data and AI technical delivery lead, not simply a project manager or high-level architect. The person will own the end-to-end delivery of data engineering, analytics, business intelligence, machine learning, predictive modeling, and GenAI solutions using Databricks as the primary platform.

The overall goal is to help Amtrak move from fixed maintenance schedules and manual data manipulation toward predictive, condition-based, and risk-based maintenance of infrastructure assets.

Primary responsibilities

The candidate will:

• Lead complex data, analytics, AI, ML, and BI initiatives from requirements through production delivery.
• Remain technically hands-on with Databricks while coordinating cross-functional delivery teams.
• Build and optimize pipelines using Databricks Workflows and Medallion Architecture.
• Ingest and transform sensor data, inspection results, maintenance history, operational data, and financial data.
• Guide predictive-maintenance models covering asset health, failure probability, and remaining useful life RUL.
• Implement Databricks governance, lineage, security, and access controls using Unity Catalog.
• Evaluate and implement capabilities such as MLflow, Delta Live Tables, and vector search.
• Build analytical frameworks, lifecycle-cost models, dashboards, and risk-scoring solutions.
• Help engineering and asset-management teams prioritize maintenance and capital investments.
• Ensure data solutions align with regulatory requirements and asset-management standards such as ISO 55000.

Must-haves skill -

Recruiters should prioritize candidates with all or nearly all of the following:

• 7+ years in data engineering, data analytics, data architecture, or asset analytics.
• 3+ years leading complex data programs, technical projects, or asset-management initiatives.
• Hands-on experience with Databricks or a comparable enterprise data-intelligence platform.
• Experience designing and delivering production data pipelines.
• Strong understanding of modern data architecture, preferably Lakehouse and Medallion Architecture.
• Experience leading data solutions from requirements and architecture through deployment.
• Ability to work directly with engineers, data scientists, business leaders, and asset-management stakeholders.
• Strong technical leadership combined with program-delivery ownership.

Strongly preferred experience -

• Rail, transit, utilities, transportation, infrastructure, manufacturing, energy, or another asset-intensive industry.
• Infrastructure, linear assets, or fixed physical assets.
• Predictive maintenance, condition monitoring, reliability engineering, or asset-health analytics.
• Sensor, telemetry, inspection, maintenance, operational, and financial data.
• Asset risk scoring, failure prediction, lifecycle-cost analysis, or remaining-useful-life modeling.
• Unity Catalog, MLflow, Delta Live Tables, Databricks Workflows, and vector search.
• ISO 55000 or comparable enterprise asset-management standards.
• Databricks Data Engineer or Machine Learning Associate/Professional certification.