Senior Principal Information System Architect

UKG

14h

About The Position

We are seeking a strategic and visionary Enterprise Architect-Data to join our Enterprise Architecture team, reporting to the VP of Enterprise Architecture. This role is a critical partner to our Business and Application architects, responsible for defining and executing the enterprise-wide data strategy, particularly in the context of a dynamic, M&A-driven environment. The successful candidate will design a scalable, secure, and resilient data ecosystem that transforms data into a trusted enterprise asset. You will be responsible for aligning the company's data architecture with its overall business strategy, ensuring that data is leveraged effectively to drive analytics, insights, and power AI-driven outcomes.

Requirements

15–20 years of experience in data architecture, data engineering, data management, or a related senior-level role.
Proven, hands-on experience leading data platform consolidation and integration initiatives following corporate mergers and acquisitions (M&A).
Deep, hands-on expertise with the Google Cloud data stack is essential, including extensive experience with BigQuery, Cloud Composer, Vertex AI, Pub/Sub, and Cloud Run.
Demonstrated experience designing data integration and Reverse ETL patterns for major enterprise SaaS platforms, specifically Salesforce.com, Microsoft Dynamics 365 (D365), and Oracle Eloqua.
Hands-on experience with both traditional enterprise ETL tools (e.g., Informatica, Talend) and modern data engineering languages and libraries (e.g., Python, Spark).
Hands-on experience with API design and management, including designing REST APIs and using API Gateways (e.g., Google Apigee, Kong) to secure and manage data access for both internal and external applications.
Deep understanding of both modern data architecture patterns (e.g., Medallion Architecture, Data Mesh) and traditional dimensional modeling techniques (e.g., Kimball, Star Schema).
Proven experience designing data architectures that support and enable advanced AI applications, including machine learning pipelines and integrations for Agentic AI.
Experience designing, contributing to, or implementing an enterprise Data Marketplace or Data Catalog.
Demonstrated experience designing and implementing robust data quality frameworks (e.g., setting up DQ monitoring, defining validation rules) and data security controls (e.g., data encryption, PII masking, column-level security) within a cloud data platform.
Excellent and proven communication, facilitation, and stakeholder management skills.

Nice To Haves

You love working on data systems and are obsessed with quality, consistency, and scale.
You enjoy solving complex problems and have a deep-thinking, analytical mindset.
You think of 'data as a product' and have experience designing data services and APIs for consumption by a wide range of applications and users.
You gain gratification from seeing your ideas take shape and become real-world solutions used by a large number of users.
You are proactive, detail-oriented, and motivated by impact at scale.
You thrive in collaborative environments and enjoy bridging business and technology perspectives.
You are an individual contributor who enjoys taking ownership and adding tangible value to the organization.
You are a seasoned professional who can work independently with minimal guidance and assistance, while still driving outcomes effectively.

Responsibilities

Lead the architectural design and strategy to ensure all enterprise data is treated as a unified asset. This involves creating a seamless consolidation and rationalization strategy that applies to all major data-centric initiatives, whether they are driven by M&A or by the development of new internal applications and services.
Partner with application development teams to provide data-centric design insights into core business applications, ensuring the data layers are designed for scalability, quality, and analytical use from the outset.
Analyze and document the current-state data landscape, including data sources, lineage, storage, and consumption patterns to identify gaps, redundancies, and modernization opportunities.
Develop and maintain the enterprise data architecture, including conceptual, logical, and physical data models, data flow diagrams, and integration patterns in alignment with industry standards.
Collaborate with business leaders, data scientists, and engineering teams to define strategic goals and translate them into data, analytics, and AI requirements.
Design and govern the target-state enterprise data architecture, incorporating modern patterns such as Medallion Architecture and principles of Data Mesh. This includes standards for the data warehouse, data lake, streaming platforms, master data management (MDM), and the platforms and patterns that support our AI/ML initiatives.
Develop and govern the enterprise API strategy for data, defining standards for REST APIs and managing data access for internal and external applications through an API Gateway.
Design and govern 'Reverse ETL' patterns that deliver analytics and AI-driven insights back into operational systems (e.g., Salesforce, Eloqua, D365).
Incorporate FinOps principles into all data architecture designs, ensuring solutions are optimized for cost-effectiveness by leveraging BigQuery best practices (e.g., partitioning, clustering), appropriate storage tiers, and efficient compute.
Partner with product managers, solution architects, and business architects to ensure data solutions are designed for scalability, performance, and security while meeting business needs.
Define and enforce enterprise-wide data governance standards, including data quality, metadata management, data security, and data privacy in partnership with the Enterprise Data and Analytics Office (EDAO).
Evaluate data-related requirements to assist in selecting the right data technologies, platforms, and tools that support a scalable, future-ready data ecosystem.
Contribute to enterprise transformation initiatives by providing data-architecture-driven insights and strategic guidance on how to best leverage data assets.
Facilitate workshops and working sessions with stakeholders to align on data definitions, standards, and architectural principles.
Utilize predefined frameworks and templates effectively, ensuring all data architecture artifacts are maintained and regularly updated.