AI Data Platform Lead

This is a fully remote position, open to applicants in Canada.

📋 Description

• Oversee the comprehensive data architecture for the Data Warehouse Foundation, crafting designs for AI-first utilization across GPT assistants, AI agents, predictive models, and operational intelligence, alongside BI and reporting.

• Spearhead data modeling initiatives across all 11 departments, creating canonical enterprise data models that facilitate cross-functional AI and analytics applications without duplication or fragmentation.

• Develop and execute the contextual intelligence layer—encompassing RAG architecture, vector store strategy, knowledge base ingestion pipelines, and processing of document and unstructured data—that underpins Agiloft's enterprise knowledge system.

• Construct and sustain the agentic data integration layer, focusing on real-time and near-real-time data access patterns, agent memory and state persistence design, orchestration data requirements, and the reintegration of agent output back into the warehouse.

• Manage the AI/ML feature layer, including the strategy and standards for feature engineering, design of training data pipelines, architecture of feature stores, and integration of model outputs—enabling predictive analytics related to churn, pipeline health, and operational forecasting.

• Design and oversee the operational data and GPT context layer, which includes structured context feed design for GPT assistants, data freshness and access SLAs for AI applications, and standards for cross-departmental data reuse.

• Lead the development of the Data Warehouse Foundation in collaboration with the external consulting team—establishing architectural standards, assessing implementation against AI-first principles, and ensuring that the five-wave build plan delivers a foundation supporting the entire intelligence architecture.

• Design and manage data ingestion, ELT/ETL, and orchestration pipelines across all source systems, ensuring reliability, performance, and cost-effectiveness.

• Establish and enforce AI data engineering standards organization-wide, covering prompt-adjacent data design, agent data access patterns, reusable pipeline components, and quality assurance processes.

• Manage data access policy design and least-privilege access controls in partnership with Security, ensuring that data accessible to AI systems is governed, auditable, and compliant.

• Define standards for data quality and monitoring processes for AI-consumed data, emphasizing that quality failures directly impact model and agent performance.

• Collaborate with the Principal Data and Integrations Architect on infrastructure design, ensuring that data modeling and AI consumption requirements are integrated into pipeline and architecture decisions from the outset—rather than being retrofitted post-build.

• Work alongside the VP FP&A and Manager of BI & Data to guarantee that the semantic and metrics layers are technically robust and cater to both AI applications and reporting necessities.

• Oversee the AI Ops data architecture roadmap, transforming business and AI use case requirements from all 11 departments into sequenced, prioritized technical tasks.

• Maintain documentation and knowledge transfer standards for all data architecture, pipelines, and integration patterns—ensuring that AI Ops-built infrastructure is reusable, auditable, and not reliant on any single individual.

• Collaborate with the AI Agent Engineer and GPT & AI Systems Lead to ensure that data infrastructure supports agent orchestration, retrieval-augmented generation, and multi-step reasoning workflows.

• Define the roadmap for data science and AI data initiatives in conjunction with the VP of AI Operations—this role operates independently of IT regarding resource allocation or prioritization, with all roadmapping managed within AI Operations.

• Assess and recommend data tools, frameworks, and platform components consistent with AI Ops' technology-agnostic, build-for-leverage philosophy.

• Perform other duties as assigned.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field is required.

• 7–10 years of experience in data engineering, data architecture, or a related technical role, with a minimum of 3 years focused on AI or ML data infrastructure.

• Extensive knowledge of modern data stack technologies—Snowflake is required; familiarity with dbt, Airflow or equivalent orchestration, and ELT/ETL pipeline design is preferred.

• Proven experience in designing data architecture for AI consumption—including vector databases, embedding pipelines, RAG systems, or feature stores—not limited to BI and reporting.

• Strong data modeling capabilities across various paradigms: dimensional modeling, normalized models, and AI-optimized schemas for agent and model utilization.

• Experience in building and managing real-time or near-real-time data pipelines for operational AI applications.

• Proficiency in Python and SQL; experience with cloud data infrastructure on AWS is essential.

• Experience in designing data access patterns and governance controls for AI systems, including least-privilege access, audit logging, and AI-specific data security considerations.

• Proven ability to lead cross-functional technical programs—translating requirements from multiple business domains into coherent, prioritized data architecture decisions.

• Excellent communication skills with the ability to convey complex data architecture decisions to non-technical executives and cross-functional stakeholders.

• Experience in the SaaS industry is required.

🏝️ Benefits

• Medical, dental, and vision insurance

• Short-term and long-term disability

• Life insurance and AD&D

• Supplemental life insurance (Employee/Spouse/Child)

• Health care and dependent care Flexible Spending Accounts

• 401(k) with company match

• Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position.

• Paid parental leave

• Voluntary benefits including pet insurance