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Tech Stack
Tools & technologiesAirflowAWSCloudETLPythonSQL
About the role
Key responsibilities & impact- 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
What you’ll need- 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
Comp & perks- 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
ATS Keywords
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Hard Skills & Tools
data architecturedata modelingAI data infrastructurereal-time data pipelinesELTETLfeature engineeringdata quality standardsdata ingestiondata access patterns
Soft Skills
strong communication skillscross-functional collaborationprogram ownershiptechnical decision-makingdocumentation management