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.