Description

Job Title: IT Data Architect 
Location: Chicago, IL – 5 Days onsite role
Type : Long Term Project

Looking for Locals only and Strong Banking Domain 

Mandatory Skills: We will look for experience with modern data platforms (Snowflake or/and data bricks), and Banking/Finance working experiences.

Job Description:

Core information & data architecture
8+ years (guideline) of experience in information architecture / data architecture in a complex environment. 
Strong command of data modeling (conceptual, logical, physical) with hands-on experience. 
Demonstrated ability to create canonical data models and cross-domain harmonization (e.g., ontology, taxonomy, semantic modeling). 
Ontology, taxonomy, semantic modeling
Practical experience designing and operationalizing taxonomies, ontologies, and controlled vocabularies. 
Familiarity with knowledge graph / semantic concepts (e.g., entity/relationship modeling). 
Metadata management & governance
Proven experience building/operating enterprise metadata capabilities: glossary, catalog, lineage. 
Strong understanding of governance concepts: data ownership, stewardship, critical data elements. 
AI-ready data strategy
Working knowledge of how GenAI/ML consumes data (e.g., need for context, definitions, quality). 
Willingness and ability to use AI to accelerate IA tasks (e.g., draft glossaries, classify content). 
Understanding of responsible AI / data usage needs: privacy, access control, retention, regulatory considerations. 
Stakeholder leadership
Strong track record leading cross-functional initiatives spanning Finance, Risk, Compliance. 
Excellent facilitation skills: can drive workshops, handle disagreement, and build alignment. 
Ability to communicate effectively with both business executives and engineers. 
Preferred qualifications
Experience in Corporate Functions domains (Finance, Risk, Compliance, Audit) and related regulatory environments. 
Experience with data product operating models, domain data ownership constructs, and metadata governance practices. 
Familiarity with common enterprise architecture practices (e.g., capability maps, domain maps). 
Success measures (first 6–12 months)
A prioritized IA roadmap aligned to AI-ready outcomes (metadata coverage, glossary completeness, etc.). 
Agreement on key cross-domain definitions and a scalable process to maintain them. 
Adoption of modeling and metadata standards across multiple programs/teams. 
Tangible improvement in data discoverability and usability for analytics/AI use cases. 
Role level
This is a mid-to-senior individual contributor role (or lead IC), requiring maturity to drive enterprise alignment. 
Junior profiles are unlikely to be successful given the stakeholder complexity and influence required.

Key Skills

Education

Not specified