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 (Google Cloud Platform)**.
* 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 Google Cloud Platform**.
* 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.