AI Data Platform Lead

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

📋 Description

• Take ownership of the complete data architecture for the Data Warehouse Foundation, focusing on AI-first consumption for GPT assistants, AI agents, predictive models, and operational intelligence, in addition to BI and reporting.

• Spearhead data modeling across all 11 departments, creating canonical enterprise data models that effectively serve cross-functional AI and analytics use cases without any duplication or fragmentation.

• Design and implement the contextual intelligence layer, including RAG architecture, vector store strategy, knowledge base ingestion pipelines, and document and unstructured data processing, which powers Agiloft's enterprise knowledge system.

• Develop and maintain the agentic data integration layer, ensuring real-time and near-real-time data access patterns, agent memory and state persistence design, orchestration data requirements, and integration of agent outputs back into the warehouse.

• Oversee the AI/ML feature layer, including feature engineering strategy and standards, training data pipeline design, feature store architecture, and model output integration, facilitating predictive analytics across churn, pipeline health, and operational forecasting.

• Design and govern the operational data and GPT context layer, which includes structured context feed design for GPT assistants, data freshness, and access SLAs for AI use cases, along with standards for cross-departmental data reuse.

• Lead the construction of the Data Warehouse Foundation in collaboration with the external consulting team, establishing architecture standards, reviewing implementation against AI-first principles, and ensuring that the five-wave build plan delivers a solid foundation for the complete intelligence architecture.

• Create and manage data ingestion, ELT/ETL, and orchestration pipelines across all source systems, ensuring they are reliable, performant, and cost-efficient.

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

• Design data access policies and implement least-privilege access controls in partnership with Security, ensuring that data available to AI systems is governed, auditable, and compliant.

• Define data quality standards and monitoring processes for AI-consumed data, recognizing 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 included in pipeline and architecture decisions from the beginning, rather than retrofitted later.

• Work alongside the VP FP&A and Manager of BI & Data to ensure the semantic and metrics layers are technically robust and support both AI use cases and reporting requirements.

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

• 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 partnership with the VP of AI Operations, with this role managing all roadmapping independently of IT in terms of resource allocation or prioritization.

• Evaluate and recommend data tooling, frameworks, and platform components in alignment with AI Ops' technology-agnostic, build-for-leverage approach.

• 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 function, with at least 3 years dedicated to AI or ML data infrastructure.

• Extensive knowledge of modern data stack technologies, specifically Snowflake; experience with dbt, Airflow or equivalent orchestration, and ELT/ETL pipeline design is essential.

• Proven experience in designing data architecture for AI consumption, including vector databases, embedding pipelines, RAG systems, or feature stores, beyond just BI and reporting.

• Strong data modeling capabilities across multiple paradigms, including dimensional modeling, normalized models, and AI-optimized schemas for agent and model consumption.

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

• Proficiency in Python and SQL, with required experience in cloud data infrastructure on AWS.

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

• Demonstrated ability to manage cross-functional technical programs, translating requirements from various business domains into coherent, prioritized data architecture decisions.

• Excellent communication skills, capable of making complex data architecture decisions clear 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