Data & AI Engineer - 90408785 - Remote

Amtrak is a company dedicated to connecting businesses and communities across the United States. The Data & AI Engineer Specialist plays a key role in delivering enterprise data and AI capabilities, transforming data requirements into scalable pipelines and services to support data-driven decisions and innovation.

Responsibilities:

• Design, develop, and maintain data pipelines, AI frameworks and integration workflows supporting analytics and AI applications
• Implement data transformation, validation, and quality controls to ensure reliability and compliance with standards
• Collaborate with architects, analysts, and data scientists to operationalize data models and business logic
• Participate in agile planning, sprint delivery, and retrospectives to continuously improve team performance
• Document technical specifications, lineage, and best practices for data assets and workflows

Requirements:

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field
• 2–4 years of hands-on experience in data engineering, integration, or software development
• Proficiency in Python and SQL with strong foundations in data and AI architecture, including feature pipelines, model serving, and inference patterns
• Understanding of ETL/ELT concepts, APIs, and data pipeline orchestration
• Strong experience building federated data and feature models; performance tuning for analytics and AI workloads; designing reusable views and features; implementing caching, row-level security, and data contracts
• Experience working within cloud or hybrid data environments
• Strong analytical, debugging, and problem-solving skills
• Demonstrated ability to work independently and deliver high-quality results within an agile framework
• Effective collaboration and communication skills across technical and business stakeholders
• Experience with data lakehouse or cloud data platforms
• Familiarity with metadata management, lineage, and observability practices
• Exposure to AI/ML pipeline development, automation frameworks, or MLOps concepts