Enterprise Information Architect

TEKsystems•Plymouth, WI

1d•$60 - $70•Hybrid

About The Position

Sargento is looking to hire an Enterprise Information Architect on a contract-to-hire basis. FTE conversion is $132-$138k with bonus potential of 15%. This role will be hybrid out of Plymouth, WI - 4 days a week on site, half day on Fridays. The Enterprise Information Architect (EIA) will serve as the strategic leader responsible for defining and advancing the organization’s enterprise information architecture. This role sits at the forefront of major initiatives—including the enterprise data and analytics platform modernization, sales & marketing analytics transformation, and a major manufacturing program—ensuring that data structures, integration designs, and information flows are built on solid architectural foundations. The EIA will partner closely with Enterprise Architecture, Data & Analytics leadership, data engineering, governance, and business stakeholders to establish conceptual data models, define data ownership boundaries, optimize data flow patterns, and enable enterprise-scale analytics. This role is not a delivery-focused or report-building position, but rather an enterprise-level thought leadership and architecture function. The ideal candidate brings deep experience in modern data architectures (Snowflake, data lakes, cloud-based warehouses), strong integration knowledge, and the ability to influence both technical teams and executive leaders. This person will help prevent downstream rework by guiding early-stage architectural decisions and ensuring enterprise alignment on how data should be structured and consumed. This role reports to Erin (Enterprise Architecture leader) and will work closely with the Enterprise Architect and Solutions Architecture teams, as well as Travis Lehn and his data team.

Requirements

8+ years in enterprise information architecture or similar strategic data leadership roles.
Experience creating conceptual and logical models for large, complex organizations.
Knowledge of ELT tools (Matillion preferred).
Ability to design integration patterns for API-driven and batch processes.
Strong facilitation skills—ability to guide conversations across engineering, governance, analytics, and business teams.
Comfort working with both structured and unstructured datasets.
Proven ability to drive alignment in ambiguous or immature architectural environments.
Excellent organizational skills, transparency, and time management.
Collaborative mindset aligned to a culture emphasizing employee engagement.
Enterprise architecture
Data architecture
system integration
data modeling

Nice To Haves

Experience with SAP ecosystems (not a pure SAP architect, but understanding data structures helps).
Experience with BI/reporting migration or modernization.
Exposure to self-service analytics enablement.
Familiarity with ThoughtSpot, BusinessObjects, or similar analytics tools.
Understanding of digital user experience principles in the context of data consumption.
Manufacturing or sales analytics domain exposure.

Responsibilities

Acts as the organizational authority for information architecture standards, governance, and enterprise data definitions.
Sets strategy and frameworks for how data is structured, owned, integrated, and consumed across functions.
Designs scalable data lake/warehouse environments.
Understands cloud data platforms, modern ELT/ETL approaches, and how to structure an enterprise data ecosystem.
Familiar with Snowflake (primary environment), Matillion (ELT), and core analytics platforms such as ThoughtSpot.
Critical given the company’s immature integration landscape—must guide integration design, data movement, API-first approaches, and cross-domain operational data flows.
Not looking for heavy custom development but rather architecture, standards, and patterns.
Works with enterprise architecture, data engineering, analytics, governance, and business stakeholders.
Must effectively communicate conceptual models, integration implications, and data ownership to business leaders.
Ability to take conceptual data models beyond theory—creating tangible, actionable models within the first 90 days.
Experience with both structured and unstructured data modeling.