Cognizant
Cloud/Data Integration Architect
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Key Purpose
Own data platform and integration architecture underpinning AI/ML and automation workloads, ensuring the data layer feeding these systems is well-designed, reliable, governed, and able to scale with growing model and agent usage.
Roles and Responsibilities
- Design data pipelines and integration patterns that feed AI/ML and automation workloads
- Architect cloud data platforms — storage, streaming, batch and real-time pipelines — sized and structured to support model training and inference at scale
- Own data governance decisions, including data quality, lineage, access control, and compliance considerations for AI-consumable data
- Ensure data pipelines feeding models and agents are production-grade: reliable, monitored, and able to scale with workload growth
- Design integration architecture between source systems, data platforms, and downstream AI/automation consumers
- Work closely with the AI Automation Architect to ensure data architecture aligns with automation and agent design requirements
- Provide architectural guidance and design review to engineers building the underlying pipelines and integrations
- Assess existing data architecture and identify gaps or risks relative to AI/ML consumption requirements
- Define data platform standards and reusable patterns across engagements
- Support capacity planning and scaling decisions as AI/automation workload volume increases
- Own technical risk assessment related to data architecture, including data quality and pipeline reliability risks
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Required Skills/Experience
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- Strong data architecture background, including data platform design and pipeline architecture
- Cloud platform expertise across Azure/AWS/GCP, particularly data services such as data lakes, warehouses, and streaming platforms
- Clear understanding of AI/ML data requirements — what "production-grade" means for data feeding models, including freshness, volume, and quality standards
- Experience with data governance frameworks, including lineage, access control, and compliance
- Integration architecture experience, including APIs, ETL/ELT, and event-driven patterns
- Ability to partner closely with AI/automation architects rather than operating in a data silo
- Experience assessing and improving legacy data architecture to support new AI/ML use cases
- Strong documentation and standards-setting ability, given the cross-engagement nature of the role
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