Cloud/Data Integration Architect

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

Required Skills/Experience

• 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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