IT Solutions Architect Sr

Role IT Solutions Architect Sr

Location Remote w/PI planning travel once a quarter at Washington DC

Duration: 12 Months+

Job Description:

10+ years of progressive IT/technology experience with a foundation in hands-on engineering delivery application development, systems integration, or data platform work before moving into architecture; candidates who have only held advisory roles will not be competitive.
5+ years as a Solutions or Enterprise Architect with demonstrated ownership of complex, multi-system solutioning in rail, transit, airlines, or transportation operations; exposure to scheduling, dispatch, crew management, asset maintenance, and train control function as a connected operational ecosystem is a plus.
Proven ability to perform structured business process analysis process mapping, As-Is/To-Be modeling, gap analysis, workflow decomposition not systems architecture in isolation; the work requires equal fluency in operational process and technical design.
Deep command of integration architecture: event-driven patterns, REST/GraphQL APIs, pub/sub and streaming (Kafka or equivalent), and data exchange in operational contexts spanning OT/IT boundaries.
Experience designing for 24/7 operational availability on cloud and hybrid platforms (AWS, Azure, or Google Cloud Platform); familiarity with the reliability, latency, and fault-tolerance requirements of safety-adjacent rail environments.
Ability to lead technical solutioning for operational data pipelines real-time streaming ingestion, event processing, and lakehouse patterns applied to train performance, delay causality, and asset health data.
Demonstrated ability to produce rigorous, execution-grade architecture artifacts: solution design documents, integration maps, API contracts, non-functional requirement matrices, and Architecture Decision Records that engineering teams can build against without interpretation overhead.
Direct engagement experience with operational stakeholders dispatchers, controllers, planners, front-line supervisors to ground architecture decisions in actual workflow context rather than idealized process models.
Familiarity with AI/ML-enabled operations: predictive maintenance, anomaly detection, scheduling optimization, or computer vision inspection pipelines; experience with lakehouse architecture (Databricks, Delta Lake, Unity Catalog) applied to operational and engineering data is a strong differentiator.
Prior contractor or consulting engagement model experience; able to deliver independently, manage scope, communicate proactively on blockers, and maintain artifact quality without close supervision including support for RFP/RFQ technical inputs, vendor evaluations, and architecture governance participation