Data Platform Architect

General Summary

Provides technical leadership for enterprise data platform design, operational architecture, platform standards, environment strategy, and platform modernization. Translates enterprise architecture direction into secure, reliable, scalable, and operationally viable platform designs that support data engineering, analytics, artificial intelligence, machine learning, business intelligence, and reusable enterprise data products across business domains.

Duties and ResponsibilitiesEssential Functions:

• Defines target architecture, reference patterns, standards, and technical roadmaps for enterprise data platforms, including lake, lakehouse, warehouse, streaming, integration, and analytics capabilities.
• Designs platform operating models, environment and workspace strategies, provisioning approaches, and reusable platform services that support multiple business domains.
• Establishes security, identity, access, privacy, network, data protection, and information lifecycle patterns with enterprise architecture, cybersecurity, data governance, and infrastructure teams.
• Translates enterprise architecture standards and business requirements into operationally viable platform designs, architecture decisions, implementation guidance, and modernization plans.
• Guides the architecture of Databricks, cloud data services, orchestration, metadata, catalog, observability, and data movement capabilities that support governed enterprise data products.
• Defines engineering patterns for continuous integration and delivery, source control, automated testing, infrastructure and configuration practices, deployment, monitoring, and operational recovery.
• Leads architecture reviews and quality assurance for platform and data engineering solutions, identifying risks, dependencies, technical debt, and opportunities for reuse and simplification.
• Evaluates emerging platform technologies and recommends adoption, integration, or retirement decisions based on strategic fit, security, reliability, scalability, interoperability, and cost.
• Partners with Data Engineering, Platform Operations, Data Architecture, Cogito, analytics, and business-aligned delivery teams to support enterprise platform onboarding and adoption.
• Guides platform modernization and operational improvement initiatives, including workspace improvements, migration patterns, performance optimization, monitoring, and support readiness.
• Documents platform architecture, standards, reference implementations, decision records, service boundaries, dependencies, and operational requirements.
• Provides technical leadership, mentoring, and consultation while promoting common standards across the federated data and analytics operating model.

Common Expectations:

• Demonstrates commitment to WellSpan values, service excellence, security, privacy, responsible data use, and stewardship of enterprise information assets.