Enterprise Data Architect

## About the Role We are seeking an experienced • • Enterprise Data Architect • • to lead the design and evolution of enterprise information architecture and reusable data products supporting large-scale modernization, analytics, and AI initiatives. The ideal candidate will bring deep expertise in • • enterprise data architecture, information modeling, cloud data platforms, data governance, data quality, and modernization • • . This individual will work closely with business leaders, product teams, engineers, data teams, and technology stakeholders to establish a sustainable enterprise data foundation while reducing duplication and data silos. This is a senior-level architecture position requiring both strategic leadership and hands-on experience designing complex enterprise data environments. ## Key Responsibilities ### Enterprise Data Architecture • Define conceptual and logical data models for enterprise business entities. • Develop canonical data models and enterprise information standards. • Establish relationships between enterprise-wide and domain-specific data assets. • Design information architecture incorporating data security, privacy, classification, and regulatory requirements. • Define enterprise data architecture principles, standards, patterns, and best practices. ### Data Modeling & Modernization • Support large-scale application and data modernization initiatives. • Analyze legacy systems and identify data assets suitable for enterprise reuse. • Develop source-to-target and source-to-domain data mappings. • Support data migration strategies and target-state architecture. • Prevent unnecessary duplication of data structures across systems and business domains. • Design and support data warehouses, data marts, and modern data architectures. ### Data Product Architecture • Identify opportunities to create reusable enterprise data products. • Define schemas, interfaces, metadata, data contracts, and quality requirements. • Establish appropriate boundaries, ownership, and stewardship models for data products. • Promote API-first and product-oriented approaches to enterprise information sharing. ### Data Governance, Quality & Metadata • Establish standards for: • Data governance • Metadata management • Data catalogs • Data lineage • Data observability • Data interoperability • Data security • Data quality • Define business data definitions and enterprise data quality expectations. • Partner with governance and business teams to establish ownership and stewardship standards. • Support implementation of data quality platforms and tooling. ### Cloud & Data Platform Architecture • Collaborate with cloud, platform, integration, and engineering teams to translate business requirements into scalable technical solutions. • Architect solutions using modern data platforms such as • • Snowflake, Databricks, and MongoDB • • . • Support enterprise data environments across • • AWS, Microsoft Azure, and/or Google Cloud Platform (GCP) • • . • Work with structured, semi-structured, and unstructured enterprise data. ### AI & Advanced Analytics Enablement • Ensure enterprise data products are discoverable, governed, secure, and suitable for AI and advanced analytics use cases. • Help establish the data foundation required for enterprise AI initiatives. • Support development of semantic layers, knowledge graphs, and natural-language access to enterprise information. • Collaborate with analytics and AI teams to ensure data architecture supports future machine learning and generative AI capabilities. ## Required Qualifications • Bachelor's degree in • • Computer Science, Information Systems, Systems Programming, Engineering • • , or a related discipline, or an equivalent combination of education and professional experience. • 10+ years of experience • • in Data Architecture, Information Architecture, or Enterprise Architecture. • 8+ years of hands-on experience • • in data architecture, data engineering, advanced database design, and data modeling. • Strong experience designing or implementing • • data warehouses and data marts • • . • Experience with • • Master Data Management (MDM) • • concepts, architectures, and tools. • 5+ years of experience • • working with modern data platforms such as: • Snowflake • Databricks • MongoDB • Strong experience with cloud-based data ecosystems using • • AWS, Azure, and/or GCP • • . • Experience designing conceptual, logical, and enterprise data models. • Experience with enterprise data governance, metadata management, data lineage, and data cataloging. • Experience developing and implementing enterprise data quality initiatives and associated platforms/tools. • Strong knowledge of data security, privacy, and regulatory requirements involving sensitive information. • Demonstrated experience supporting • • large-scale modernization or digital transformation initiatives • • involving multiple domains and stakeholders. • Strong understanding of enterprise integration and API-based architectures. • Excellent communication, documentation, stakeholder management, and presentation skills. • Ability to operate independently, resolve ambiguity, develop work plans, and influence teams without direct authority. ## Preferred Qualifications • Previous experience as an: • Enterprise Data Architect • Enterprise Information Architect • Principal Data Architect • Principal Architect • Data Solution Architect • Enterprise Solution Architect • Experience in • • public sector, healthcare, or financial services • • environments. • Experience working with unstructured data. • Experience implementing reusable enterprise data products. • Knowledge of data contracts and data-product architectures. • Experience with semantic models or semantic layers. • Knowledge of knowledge graphs and enterprise ontology concepts. • Experience supporting data platforms designed for • • AI, machine learning, or generative AI • • applications. ## What Success Looks Like The successful Enterprise Data Architect will help: • Establish enterprise-wide information architecture principles and standards. • Create reusable data models and enterprise data products. • Support modernization programs with scalable target-state data architecture. • Establish metadata, governance, ownership, and data quality standards. • Reduce duplication and information silos across enterprise applications. • Improve reuse of common data assets across business domains. • Build a sustainable foundation for enterprise analytics, automation, and AI. ## Work Arrangement This position follows a • • hybrid schedule in Harrisburg, Pennsylvania • • , with approximately • • one day per week onsite • • , typically Tuesday, Wednesday, or Thursday. Candidates should be comfortable participating in a multi-stage interview process that may include virtual interviews and a final in-person interview in Harrisburg.