TECHNICAL ARCHITECT

Role overview

Architecture and platform governance

• Own architecture across data engineering, AI/ML, IAM/security, QA, analytics, and client IT workstreams.
• Define blueprints for ingestion, transformation, master data management, analytics, AI/ML, dashboards, and write-back to source systems.
• Map customer needs to platform capabilities, manage expectations, and raise roadmap conversations for capability gaps.
• Design integrations across EHR systems, databases, SFTP, HL7/FHIR, claims/EDI, IT operations, and security telemetry.
• Define connector sequencing, protocols, API gateways, network constraints, and error handling; coordinate access and connector capabilities.
• Design environment topology, workspace isolation, CI/CD pipelines, promotion gates, rollbacks, disaster recovery, and RTO/RPO targets.
• Establish infrastructure-as-code, automated testing and deployment, monitoring, and alerting practices.
• Define deployment models for AI agents and dashboards.
• Manage escalations and technical risks; maintain architecture decision logs and risk registers.
• Chair technical steering committees, assess go-live readiness, advise customer technology executives, and liaise with the platform vendor.
• Create architecture and integration designs for proposals and RFPs, and support technical solution presentations.

Required skills and qualifications

• 12–18 years of enterprise or solution architecture experience, with ownership from design through production deployment.
• Experience designing large-scale data platforms, including lakes or lakehouses, ETL/ELT, real-time and batch processing, and API integrations.
• Hands-on experience with Databricks or Snowflake; familiarity with AWS or Azure data services.
• Strong DevOps and CI/CD experience, including infrastructure as code, deployment pipelines, and containerization.
• Experience managing environments, promotion workflows, rollback strategies, and production incidents.
• Working knowledge of data governance, data stewardship, metadata management, and data catalogs.
• Experience with exploratory data analysis, data profiling, statistical analysis, data quality assessment, and anomaly detection.