Data Solution Architect

Vertiv Group • Westerville, OH

1d

About The Position

Vertiv is building a modern, AI-ready data ecosystem to support analytics, domain data products, and digital applications. The Data Solution Architect is a hands-on, execution-focused role responsible for translating data architecture standards into working solutions and owning solution-level design and implementation across Snowflake, data products, APIs, and cloud integration. The architect designs and prototypes solutions and guides implementation in collaboration with engineers and product teams.

The role designs end-to-end Snowflake solutions for analytics, domain data products, AI use cases, and insight generation. It translates business needs into implementable designs spanning ingestion, Silver-to-Gold and data product layers, output ports, and consumption patterns. The architect works closely with engineering teams on schemas, models, transformations, SQL, ELT patterns, and Snowflake capabilities; designs API-first access and integrations for data products in the Data Marketplace; and defines cloud integration patterns for AWS-hosted platforms.

The position also embeds data quality, metadata, lineage, observability, monitoring, security, and scalability into solution designs. Working with the Principal Data Architect, the architect helps ensure data products are governed, consistently modeled, and AI-ready. The role actively uses AI tools to accelerate design, prototyping, code quality, documentation, and validation, while encouraging AI-assisted development practices across engineering teams.

Requirements

• 5–7+ years of experience in data engineering, data modeling, solution architecture, or data platform roles.
• Strong hands-on expertise with Snowflake, including SQL, data modeling, and performance.
• Experience designing end-to-end data solutions, rather than only individual pipelines.
• Strong understanding of data products and modern data architecture patterns.
• Strong understanding of cloud platforms; AWS is preferred.
• Strong understanding of API-first and microservices-style integration.
• Experience working closely with engineering teams in agile delivery models.
• Strong documentation and communication skills, including designs, trade-offs, and diagrams.

Nice To Haves

• Experience enabling AI or analytics use cases on data platforms leveraging enterprise data.
• Prior experience with Snowflake, Data Marketplaces or catalogs, or self-service analytics.
• Experience in manufacturing or engineering domains.
• Snowflake or cloud certifications.

Responsibilities

• Design end-to-end Snowflake data solutions for analytics, domain data products, AI use cases, and insight generation.
• Translate business use cases into practical, implementable solution designs.
• Design solutions across ingestion, Silver-to-Gold and data product layers, output ports such as views, APIs, and feeds, and consumption patterns including Marketplace, BI, and AI.
• Support and design Code Red use cases requiring data architecture for design and implementation.
• Work hands-on with engineering teams to define schemas, data models, and transformations; design performant SQL and ELT patterns; review pipelines, queries, and data models; and guide use of Snowflake capabilities.
• Design API-first access patterns for data products exposed through the Data Marketplace.
• Partner with Full-Stack Engineers to separate data logic from application logic and ensure secure, scalable data access.
• Define integration patterns between Snowflake, AWS services, Marketplace backend, and APIs.
• Design data solutions for AWS-hosted platforms, including IAM, Okta, access control, and event-driven and batch data flows.
• Ensure solution designs provide security, scalability, and operational robustness.
• Embed data quality checks, metadata, lineage, observability, and monitoring in solution designs.
• Work with the Principal Data Architect to ensure data products are AI-ready, consistent, well-modeled, governed, scalable, and aligned with defined data standards.
• Use AI tools to accelerate solution design and prototyping, improve code quality and documentation, and generate and validate SQL, APIs, and test cases.
• Encourage AI-assisted development practices across engineering teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed