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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 Responsibilities
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. Participates in architecture governance, planning, incident and problem reviews, roadmap development, and continuous improvement. Maintains current knowledge of cloud data platforms, platform engineering, healthcare data practices, security, governance, and emerging data and AI technologies.
Qualifications Minimum
High School Diploma or GED Required Work
10+ years Required Courses and Training: Databricks Data Engineer Associate/Professional or equivalent cloud data certification Upon Hire Preferred Microsoft Power BI Data Analyst Associate or equivalent BI certification Upon Hire Preferred Knowledge, Skills, and Abilities: Advanced knowledge of enterprise data platform architecture, data lakes, lakehouse and warehouse designs, distributed computing, real-time data distribution, integration, metadata, and platform operating models Proficiency designing modern cloud data architectures using Azure, AWS, or Google Cloud and platforms such as Databricks, Snowflake, Apache Spark, Kafka, Hadoop, Oracle, relational databases, and NoSQL technologies Strong understanding of security and access architecture, privacy, networking, identity, encryption, secrets management, data protection, governance, catalog capabilities, and enterprise risk controls Knowledge of platform engineering practices including environment and workspace strategy, continuous integration and delivery, source control, automated testing, observability, monitoring, reliability, and disaster recovery Ability to create architecture roadmaps, standards, reference patterns, decision records, technical designs, and reusable services that simplify the technology landscape and align platform capabilities with business needs Ability to evaluate traditional and emerging data and AI technologies, identify architectural tradeoffs, and recommend solutions based on strategic fit, interoperability, scalability, reliability, security, readiness, and cost Benefits Offered: Comprehensive health benefits Flexible spending and health savings accounts Retirement savings plan Paid time off (PTO) Short-term disability Education assistance Financial education and support, including DailyPay Wellness and Wellbeing programs Caregiver support via Wellthy Childcare referral service via Wellthy
Full time · Shift1 - Day shift · 40 hours per week
About the role
Defines target architecture, reference patterns, standards, and technical roadmaps for enterprise data platforms, including lake, lakehouse, warehouse, streaming, integration, and analytics capabilities.