Job Description

## 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.