AI Data Platform Lead

About Agiloft

As the most trusted global leader in data-first contract lifecycle management (CLM) software, Agiloft helps organizations manage the end-to-end process of proposing, negotiating, signing, and leveraging contracts using our flexible Data-first Agreement Platform (DAP). With contract data as the foundation, customers quickly and collaboratively reach agreement and leverage contract visibility to thrive with competitive advantage. Employing powerful, pragmatic artificial intelligence as a legal force multiplier, and robust integration capabilities as a data liberator, organizations around the world trust Agiloft’s certified implementers to deliver connected, intelligent, and autonomous solutions across the entire contract lifecycle. We believe that the way to build the strongest, most vibrant place to work is to bring in individuals from all walks of life, and to support them in bringing their authentic selves to their day, every day. Our working philosophy is that “EX = CX”: when employee experience is excellent, so is customer experience. We support multiple Employee Resource Groups (ERGs), and offer a working environment that supports healthy work/life balance, including floating holidays and a quarterly, no-questions-asked wellness day.

Position Overview

The AI Data Platform Lead is the foundational technical role within AI Operations responsible for designing, building, and governing the cross-departmental data infrastructure that powers Agiloft's AI transformation. This role owns the full data engineering scope required to make the Data Warehouse Foundation serve not only business intelligence and reporting, but the complete spectrum of AI use cases, GPT assistants, AI agents, predictive analytics, real-time operational intelligence, and the contextual intelligence layer that underpins the organization's intelligent operating model. This role is the prerequisite for all downstream data consumers, including BI and reporting functions, to operate effectively. The AI Data Platform Lead reports to the VP of AI Operations and is a core member of the AI Operations team. This role is allocated fully within AI Operations and is managed, roadmapped, and prioritized by the VP of AI Operations. Any allocation outside of the AI Operations-designated resource percentage requires explicit agreement with AI Operations leadership. This role is distinct from and complementary to the Principal Data and Integrations Architect, who owns the infrastructure layer, DW architecture design, pipeline build and maintenance, source system integrations, and platform reliability. The AI Data Platform Lead operates at the layer above infrastructure: owning what the data means, how it is modeled for AI and analytics consumption, whether it is trustworthy and fit for purpose, and how it connects to the intelligence layer that GPT assistants, agents, and predictive models depend on. The analogy is direct: the Principal Data and Integrations Architect builds and maintains the roads. The AI Data Platform Lead owns where the roads go, what travels on them, and whether what arrives at the destination is clean, modeled correctly, and ready for AI consumption. This is not a traditional data engineering or BI role. It sits at the intersection of data science, AI infrastructure, and data governance, requiring someone who understands that in an AI-first organization, data quality and data modeling are not reporting concerns. They are the foundation of every intelligent system the organization depends on.

Job Responsibilities

• 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.
• Additional duties as assigned.

Required Qualifications

• 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.

Diversity & Inclusion

Background checks will be conducted on an ongoing basis every 3 years or as needed for individuals in this role. Ensuring a diverse and inclusive workplace is our priority. We are committed to an environment of acceptance where you are free to bring your full self to work. All employment decisions at Agiloft are based on business needs, job requirements, and individual qualifications without regard to race, color, religion or belief, national or social ethnic origin, sex, age, sexual orientation, gender identity and/or expression, parental status, marital status, Veteran status, or any other status protected by the laws or regulations in the locations where we operate. If you have a need that requires accommodation during the recruiting process, please let us know by contacting Director, Talent Acquisition, Brad Toothman at brad.toothman@agiloft.com. Applicants from underrepresented groups such as minorities, veterans, or individuals with disabilities encouraged to apply. Applications will be reviewed as submitted. There will be no application deadline for this opportunity.

Key skills/competency

• AI Data Platform Lead
• Data Architecture
• Data Modeling
• Data Governance
• AI/ML Data Infrastructure
• ETL/ELT Pipeline Design
• Snowflake
• Python
• SQL
• AWS Cloud Infrastructure