Key skills
PythonSQLData EngineeringAI ArchitectureData Pipeline OrchestrationETL/ELTCloud Data PlatformsData LakehouseAI/ML Pipeline DevelopmentMLOpsMetadata ManagementData LineageData ObservabilityAIMLETLELTAnalyticsCachingAgileCommunicationCollaboration
About this role
Amtrak is a leading transportation company that connects businesses and communities across the United States. They are seeking a Data & AI Engineer Specialist to design, build, and maintain data and AI systems that enable data-driven decisions and innovation within the organization.
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