Principal Data Architect

Vertiv•Westerville, OH

About The Position

Vertiv is building a modern, intelligent data ecosystem that powers world‑class operations, customer experiences, and AI‑driven decision-making. As we accelerate our digital and AI transformation, high‑quality, governed, and accessible data is at the center of how we operate. We are seeking a Principal Data Architect for our Enterprise Data group. This role is a strategic, hands-on technical leader responsible for designing and governing the enterprise data architecture that powers Vertiv’s Data Platform for Analytics, AI, Domain Data Products, and Data Management capabilities. This role anchors the adoption of Snowflake Data & AI platform capabilities, ensures alignment with business domain needs (e.g., Order-to-Cash, Finance, Supply Chain), and establishes the architectural foundation for data quality, data products, metadata, and AI-ready capabilities. The Principal Data Architect partners closely with Engineering, Product Management, Source System teams and business leaders to define scalable patterns, reusable components, and data models that accelerate adoption of trustworthy, governed, high-quality data in our Enterprise Data Lake. This role is ideal for a solid technical leader who thrives at the intersection of business & data architecture, and hands-on technologist – who can demonstrate quick workable prototypes with hands-on approach to overall Data Architecture patterns. The Enterprise Data team is responsible for building Vertiv’s foundational data ecosystem that enables the company to scale AI, analytics, and operational intelligence. Our mission is to ensure that: Data is well‑managed and structured through automated controls and consistent data practices. Data is easily available, discoverable and usable through well-designed data products, discoverability, metadata, and semantic consistency.

Requirements

10+ years of experience in data architecture, data engineering, or software engineering roles.
Deep expertise with Snowflake, SQL, Lakehouse/Data Lake architectures, and cloud platforms (AWS preferred).
Proven experience designing conceptual/logical data models, canonical models, and domain data products.
Strong understanding of MDM, Data Quality, metadata, lineage, data governance and data management frameworks.
Hands-on experience with ELT/ETL frameworks, real-time/streaming, orchestration tools, BI and Analytics tools.
Ability to translate business processes (manufacturing related like O2C, Finance, Supply Chain) into scalable data architectures.

Nice To Haves

Experience enabling AI/ML, vector databases, or agent-based systems.
Prior experience with regulated or global manufacturing environments.
Background in solution architecture or enterprise architecture.
Certifications in Snowflake, cloud, or data management frameworks

Responsibilities

Define and maintain Vertiv’s enterprise data architecture framework, including conceptual, logical, and physical models across domains.
Lead end-to-end architectural design for our Enterprise Data Lake (powered by Snowflake), DQ tooling, and stable Data pipelines enabling real-time data availability for related use cases.
Establish standards for Data-as-a-Product, including metadata, lineage, ownership, and interoperability.
Translate business use cases into data architecture roadmaps aligning with enterprise priorities (O2C, Finance Transformation, AI use cases).
Architect scalable ingestion, transformation, orchestration, and storage/processing layers using Snowflake, Kafka/streaming, and modern ELT patterns.
Define reusable patterns for dynamic tables, ingestion zones, and domain data products.
Provide architectural direction for engineers building pipelines, APIs, data products, and semantic layers.
Lead design and rollout of data quality measurement frameworks (profiling, rules, cleansing, dimensions, monitoring).
Ensure data models and pipelines embed data reliability and observability from the start.
Architect data foundations to enable Agentic AI, predictive analytics, and generative AI.
Define and promote patterns for feature engineering, vector storage, and ML-ready data sets.
Work with Data Scientists and AI teams to ensure architecture supports scalable model training, serving, and feedback loops.
Partner with Cybersecurity and IT to define access models, data protection, and governance guardrails.
Ensure architecture aligns with regulatory, financial reporting, and audit requirements.
Drive consistent use of cataloging, lineage, and metadata management tools.
Serve as a senior technical mentor to data engineers, solution architects, and product teams.
Introduce best practices, emerging patterns, and modern architectural approaches.
Influence stakeholders across business and technology with strong communication, storytelling, and technical depth