The Senior Solution Architect – EHR, Clinical Integration & Automation is responsible for architecting and providing technical leadership for complex enterprise healthcare solutions involving Electronic Health Record (EHR) systems, clinical applications, cloud platforms, data integration, APIs, event-driven architectures, automation technologies, and enterprise applications. This role will lead technical discovery and solution architecture from initial business requirements through detailed design, development, testing, deployment, and production stabilization.
The Senior Solution Architect will work closely with clinical/EHR application teams, Automation Analysts, Full Stack Developers, Data Engineers, Application Engineers, QA Engineers, infrastructure teams, security teams, and business stakeholders to translate complex clinical and operational requirements into secure, scalable, reliable, and supportable technical solutions.
This is a hands-on technical architecture role. The successful candidate must have significant prior experience designing and developing enterprise solutions and must be capable of going beyond conceptual architecture. The architect should be able to evaluate code-level and integration-level designs, analyze data and system interactions, prototype technical approaches, when necessary, troubleshoot complex issues, and provide detailed technical direction to engineering teams. The role requires strong experience with modern enterprise technologies including Google Cloud Platform (Google Cloud Platform), .NET, APIs, Kafka/event-driven architectures, data engineering, SQL, CI/CD, and automation/RPA technologies, combined with strong experience integrating with EHR and healthcare technology ecosystems. Required Skills- Data warehouse and lakehouse platforms (Snowflake, Databricks, Synapse, Redshift, BigQuery). ETL/ELT design and pipeline orchestration. Distributed processing (Spark, SQL, Python) Streaming and event-driven architecture (Kafka, Event Hubs, Kinesis). Data integration (APIs, change data capture) Data quality and master data management DevOps, CI/CD and infrastructure as code. Analytics and BI enablement (Power BI, semantic layers). AI/ML data pipelines and feature stores. Architecture documentation and design reviews.
