AI Data Platform Lead

ID: 9378

Type: Full-time

Category: Others

Company Name: Agiloft

Location: Canada

Education Level: Senior (5-10 years)

Job Description

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.

Top analysts like Gartner, Forrester, and IDC agree, all showing Agiloft as a leader in the CLM space. Our no code platform is easily managed and administered by business users, which is why Agiloft is the contract you keep: nearly a full 100% of new customers are satisfied with their initial implementations, and some 97% of customers renew every year. Ours is a growing, vibrant, successful company that is at the forefront of a market that is becoming a must-have for all organizations.

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.

Other 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.

Preferred Qualifications

Experience in private equity-backed SaaS organizations.

Experience with agentic AI frameworks — LangGraph, Mastra, or equivalent — and the data infrastructure requirements they create.

Experience building or operating RAG architectures at production scale, including vector store selection, chunking strategy, retrieval optimization, and evaluation.

Experience with agent memory architectures and state persistence design for multi-step AI workflows.

Familiarity with AI governance and compliance requirements for data used in automated decision-making.

Experience supporting investment board or executive-level AI progress reporting from a technical infrastructure perspective.

Experience with Tines or equivalent no-code/low-code orchestration platforms for simple agent pipelines.

Exposure to contract lifecycle management, legal tech, or professional services data domains.

Agiloft offers a comprehensive benefits package for US employees including but not limited to the following:

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

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.

Company Information

Company Name: Agiloft

Company Website: https://www.agiloft.com

Company Address: 303 Twin Dolphin Drive Redwood City, California 94065 United States

Agiloft is a privately held enterprise software company that develops and delivers configurable, no-code contract lifecycle management (CLM) and business process automation solutions designed to streamline how organizations create, manage, execute, and analyze contracts and related commercial documents. The company’s core offering centers on a highly configurable platform that organizations use to automate contract authoring, approval workflows, obligation management, third-party relationship oversight, and post-signature lifecycle events. Agiloft positions its product as a unified system of record for contracts and contract-related transactions, emphasizing speed of deployment, flexibility of configuration without custom code, and built-in governance and compliance functionality. At the center of Agiloft’s solution set is its contract management suite, which provides an organized contract repository with full-text search, versioning, and audit trails. The suite supports document assembly and clause libraries to accelerate contract drafting, configurable workflow and approval routing to enforce business rules, and automated notifications and milestone tracking to manage renewals and obligations. Agiloft’s platform is designed to integrate with commonly used enterprise systems—such as CRM platforms, procurement and ERP systems, identity providers, and electronic signature tools—enabling contracts and related data to flow between systems without manual re-entry. Integrations with providers like Salesforce, Microsoft, and widely used e-signature vendors are commonly implemented by Agiloft customers to provide end-to-end automation across the contracting lifecycle. A key differentiator emphasized in Agiloft’s product positioning is its no-code/low-code configuration environment. The platform exposes a visual configuration layer—often described as no-code—where administrators and business users can model data objects, define workflows, build conditional logic, design user interfaces, and construct reports without writing traditional custom code. This approach aims to reduce implementation time, lower ongoing maintenance complexity, and empower internal teams to adapt processes as business needs evolve. The configurable architecture supports a broad set of use cases beyond core contract management, enabling customers to automate related commercial processes such as vendor management, purchase order workflows, statements of work, SLA tracking, and compliance processes. Agiloft offers deployment flexibility, with options for cloud-based SaaS deployments hosted in commercial cloud environments as well as on-premises installations where required for regulatory or security reasons. The company provides professional services, implementation guidance, and support to assist customers with process design, data migration, integration, configuration, training, and change management. For organizations with specialized needs, Agiloft’s services organization can build complex workflows and bespoke integrations, while the platform’s no-code tools allow internal teams to evolve those solutions post-implementation. Security, auditability, and regulatory compliance are central facets of the Agiloft solution, reflected in features such as role-based access control, detailed audit logs, document-level security, encryption in transit and at rest (as applicable to deployment model and customer requirements), and the ability to demonstrate controls for internal and external audits. Contract analytics and reporting capabilities enable legal, procurement, finance, and operational teams to extract insights about contract obligations, financial exposure, renewal risk, and supplier performance. These reporting features typically include dashboards, standard and custom reports, and exportable datasets to support executive decision-making and compliance monitoring. In recent years, Agiloft has incorporated advanced capabilities, including AI-assisted contract review and analytics, natural language processing features to surface key terms and obligations, and machine learning models to assist in clause classification and risk scoring. These features are intended to accelerate review cycles, highlight high-risk provisions for legal attention, and provide automated extraction of key contract metadata. Agiloft’s platform can be configured to include automated clause libraries and playbooks that guide negotiators and enforce preferred language across the contracting process. Agiloft serves a diverse set of industries, including legal departments, procurement and sourcing organizations, IT, healthcare, financial services, manufacturing, energy, and government entities. Typical users include corporate legal teams looking to reduce manual contract handling, procurement organizations seeking faster sourcing cycles and better compliance, and business units that require systematic contract management to protect revenue and control risk. Agiloft’s emphasis on configurability and enterprise-grade controls makes it applicable to both mid-market companies and large enterprises with complex contracting portfolios and compliance requirements. The company’s solution ecosystem commonly includes built-in templates, clause and clause library management, e-signature integrations, conditional authoring, obligation and milestone tracking, automated approval routing, supplier and third-party onboarding modules, and robust integration frameworks such as REST APIs and pre-built connectors. Agiloft maintains a customer success focus through support offerings, training resources, and professional services to help organizations achieve measurable outcomes like reduced contract cycle times, improved compliance, fewer missed renewals, and enhanced visibility into contractual risk. Overall, Agiloft positions itself as a flexible, configurable alternative to heavily customized contract management implementations, aiming to deliver rapid time to value for contract automation while supporting enterprise-grade security, auditability, integrations, and extensibility. Its platform is marketed to organizations that require a high degree of configurability and control over contracting processes, offering a single system to centralize contract data, automate workflows, and provide analytics to manage commercial relationships more effectively.

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