The Enterprise Knowledge Architect is a strategic business development and client advisory role focused on driving growth across Cognizant's Software Engineering Group (SEG) portfolio. Design and govern enterprise-wide knowledge architectures that integrate AI native data engineering AI agent solutions and cloud platforms to enable scalable intelligent applications for business teams. Apply deep enterprise architecture expertise to shape standards improve data driven decision making and support property and casualty insurance initiatives in a hybrid day shift role.
Key Responsibilities:
- Define and maintain an enterprise knowledge architecture strategy that aligns AI native data engineering practices with long term business objectives and regulatory expectations in a global hybrid work environment.
- Design and document end to end data flows that enable AI agents and analytical applications to consume curated high quality and compliant data sets across multiple cloud environments and on premises systems.
- Lead the design of AI native data engineering patterns that standardize ingestion transformation and storage of structured and unstructured data while optimizing performance reliability and cost efficiency.
- Coordinate cross functional solution design sessions that translate complex business needs into enterprise architecture blueprints covering applications data integration security and infrastructure layers.
- Establish governance frameworks for metadata master data and knowledge assets that improve findability reuse and traceability for data scientists engineers and business stakeholders.
- Define guardrails for AI agent engineering including data access patterns context management approaches and lifecycle controls that ensure responsible use of intelligent automation.
- Collaborate with cloud solution teams to architect scalable secure and resilient platforms that host AI workloads data pipelines and knowledge repositories across multiple cloud providers.
- Develop reference architectures and reusable patterns that guide project teams in implementing standardized data models integration approaches and AI service interfaces.
- Evaluate emerging tools and frameworks in AI engineering data platforms and cloud services to recommend adoption paths that strengthen the enterprise knowledge ecosystem.
- Partner with property and casualty insurance domain experts to shape data and knowledge models that support underwriting claims pricing and risk management insights.
- Document architecture decisions standards and guidelines in accessible formats that enable consistent implementation by technical teams and clear understanding by business partners.
- Monitor solution implementations for conformance with enterprise architecture principles and provide remediation guidance that balances innovation with operational stability.
- Mentor architects and senior engineers on enterprise architecture practices AI native data patterns and cloud design principles that improve solution quality and long-term maintainability.
Work model
At Cognizant, we strive to provide flexibility wherever possible, and we are here to support a healthy work-life balance through our various well-being programs. Based on this role's business requirements, this is an onsite position requiring 3 days a week in a client or Cognizant office in Toronto, Ontario
Required Skills and Qualifications:
- Demonstrate extensive experience applying enterprise architecture methods to large scale data and analytics programs using modern cloud platforms and integration technologies.
- Bring deep hands-on expertise in AI native data engineering including design of scalable pipelines orchestration workflows and data quality controls across complex source landscapes.
- Apply robust AI agent engineering skills to architect intelligent agents that operate on enterprise data with robust context handling observability and alignment to governance policies.
- Use solid cloud solution architecture background to design secure cost effective and performant environments for data platforms AI workloads and knowledge services in hybrid models.
- Leverage certified system architect training to enforce structured design practices modeling techniques and architecture documentation standards across initiatives.
- Offer practical understanding of property and casualty insurance processes and data structures to shape doma