AI Data Platform Lead

🕒 May 5

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Agiloft

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 1991

🏢 Enterprise

☁️ SaaS

🤖 Artificial Intelligence

💰 $45M Private Equity Round on 2020-08

Enterprise • SaaS • Artificial Intelligence

Agiloft is a leading provider of enterprise contract lifecycle management (CLM) solutions designed to streamline and enhance the contracting process. Their Data-first Agreement Platform is a force multiplier that helps organizations reach agreements quickly and collaboratively, leveraging data to operate smarter and more transparently. Agiloft's offerings include integration hubs and an AI platform tailored for business services, healthcare, legal professionals, and procurement. With a high customer retention rate and recognition as a leader in the Gartner Magic Quadrant for CLM, Agiloft is known for its user-friendly no-code interface and robust service and support. The company emphasizes direct relationships and trust, promising to help clients 'Agree and Thrive.

📋 Description

• Own the end-to-end data architecture for the Data Warehouse Foundation, designing for AI-first consumption across GPT assistants, AI agents, predictive models, and operational intelligence — in addition to BI and reporting. • Lead data modeling across all 11 departments, designing canonical enterprise data models that serve cross-functional AI and analytics use cases without 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 — that powers Agiloft's enterprise knowledge system. • Build and maintain the agentic data integration layer: real-time and near-real-time data access patterns, agent memory and state persistence design, orchestration data requirements, and agent output integration back into the warehouse. • Own the AI/ML feature layer — feature engineering strategy and standards, training data pipeline design, feature store architecture, and model output integration — enabling predictive analytics across churn, pipeline health, and operational forecasting. • Design and govern the operational data and GPT context layer, including structured context feed design for GPT assistants, data freshness and access SLAs for AI use cases, and cross-departmental data reuse standards. • Lead the Data Warehouse Foundation build in partnership with the external consulting team — setting architecture standards, reviewing implementation against AI-first principles, and ensuring the five-wave build plan delivers a foundation that serves the full intelligence architecture. • Design and manage data ingestion, ELT/ETL, and orchestration pipelines across all source systems, ensuring reliability, performance, and cost efficiency. • Establish and enforce AI data engineering standards across the organization — prompt-adjacent data design, agent data access patterns, reusable pipeline components, and quality assurance processes. • Own data access policy design and least-privilege access controls in partnership with Security, ensuring data made available to AI systems is governed, auditable, and compliant. • Define data quality standards and monitoring processes for AI-consumed data, where quality failures have direct impact on model and agent performance. • Partner with the Principal Data and Integrations Architect on infrastructure design, ensuring data modeling and AI consumption requirements are incorporated into pipeline and architecture decisions from the start — not retrofitted after build. • Partner with the VP FP&A and Manager of BI & Data to ensure the semantic and metrics layers are technically sound and serve both AI use cases and reporting requirements. • Manage the AI Ops data architecture roadmap, translating 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 AI Ops-built infrastructure is reusable, auditable, and not dependent on any single individual. • Collaborate with the AI Agent Engineer and GPT & AI Systems Lead to ensure data infrastructure supports agent orchestration, retrieval-augmented generation, and multi-step reasoning workflows. • Define the roadmap for data science and AI data work in partnership with the VP of AI Operations — this role does not take direction from IT on resource allocation or prioritization. All roadmapping is managed within AI Operations. • Evaluate and recommend data tooling, frameworks, and platform components in alignment with AI Ops' technology-agnostic, build-for-leverage approach. • Other duties as assigned.

🎯 Requirements

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related technical field required. • 7–10 years of experience in data engineering, data architecture, or a related technical function, with at least 3 years focused on AI or ML data infrastructure. • Deep expertise in modern data stack technologies — Snowflake required; experience with dbt, Airflow or equivalent orchestration, and ELT/ETL pipeline design. • Demonstrated experience designing data architecture for AI consumption — including vector databases, embedding pipelines, RAG systems, or feature stores — not only for BI and reporting. • Strong data modeling skills across multiple paradigms: dimensional modeling, normalized models, and AI-optimized schemas for agent and model consumption. • Experience building and operating real-time or near-real-time data pipelines for operational AI use cases. • Proficiency in Python and SQL; experience with cloud data infrastructure on AWS required. • Experience designing data access patterns and governance controls for AI systems, including least-privilege access, audit logging, and AI-specific data security considerations. • Demonstrated ability to own cross-functional technical programs — translating requirements from multiple business domains into coherent, prioritized data architecture decisions. • Strong communication skills with the ability to make complex data architecture decisions legible to non-technical executives and cross-functional stakeholders. • SaaS industry experience 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