VP, AI Data Architect

Role Overview

• Define the target-state Data & AI Foundations architecture supporting agentic AI use cases, including RAG pipelines, enterprise knowledge graph or metadata layer, data products, and AI-ready datasets.
• Own the strategy and roadmap for making key enterprise data sources 'AI-ready': curation, quality, metadata, access patterns, latency requirements, and retention.
• Partner with source system owners (core servicing, CRM, collections, risk, fraud, etc.) to define data contracts, SLAs, and integration patterns that support downstream RAG and analytics.
• Design and govern canonical data models and semantic layers used by RAG pipelines, memory stores, and analytics to ensure consistency across agents and domains.
• Lead the design of RAG data infrastructure on cloud (e.g., PostgreSQL, Redshift, vector stores, object storage) and ensure it aligns with performance, cost, and compliance constraints.
• Define and implement RAG evaluation strategies including retrieval quality metrics, ranking and re-ranking optimization, relevance scoring, and A/B testing frameworks for continuous improvement.
• Establish data preparation and curation pipelines for model fine-tuning, including dataset selection, labeling strategies, quality validation, versioning, and compliance with model risk policies.
• Design and optimize retrieval strategies for RAG systems: chunking approaches, embedding models, indexing techniques, ranking algorithms, re-ranking logic, and hybrid search patterns.
• Build and maintain robust data pipelines (batch and streaming) that ingest, transform, enrich, and deliver data into RAG systems, vector stores, feature stores, and agent contexts with appropriate SLAs.
• Collaborate with the Enterprise AI Platform team on how data services (RAG APIs, feature stores, metadata services) are exposed as platform primitives for agent builders.
• Define and enforce data governance policies for AI: data classification, lineage, access controls, PII handling, retention, and usage logging for AI workloads.
• Partner with AI Governance/Model Risk and InfoSec/AppSec to ensure data usage in prompts, context, and tools adheres to policies, including regulatory, privacy, and model risk requirements.
• Establish data quality and observability practices for AI data: data SLAs, freshness, completeness, drift detection, and business rule validation tied to AI outcomes.
• Drive adoption of metadata and catalog tools so platform and agent teams can discover, understand, and safely consume datasets and RAG endpoints.
• Define and oversee patterns for integrating external data (third-party, public, partner data) into AI workflows, including licensing checks, quality assessment, and monitoring.
• Perform other duties and/or special projects as assigned.

Requirements

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience)
• 12+ years of experience across data engineering, data architecture, or analytics platforms, with at least 5+ years in cloud data platforms and enterprise data leadership roles in lieu of a degree
• 14+ years of experience across data engineering, data architecture, or analytics platforms, with at least 5+ years in cloud data platforms and enterprise data leadership roles.
• Strong experience with modern cloud data stacks (e.g., data warehouses like Redshift/Snowflake/BigQuery, relational databases like PostgreSQL, and object storage) and their use in analytics and AI.
• Hands-on experience with vector databases and search technologies (for example PostgreSQL pgvector, Pinecone, OpenSearch, or similar) to support RAG and semantic search workloads.
• Demonstrated expertise in designing and governing data models, semantic layers, and data products that serve multiple consuming applications and analytics teams.
• Hands-on experience designing or supporting RAG architectures including chunking strategies, embedding pipelines, retrieval optimization, ranking/re-ranking, and evaluation frameworks.
• Solid understanding of LLM and agentic AI patterns (prompts, tools, RAG, memory) and how data quality and structure impact AI behavior and performance.
• Proven experience building data pipelines for AI/ML use cases including ETL/ELT workflows, streaming data integration, and data preparation for model training and fine-tuning.
• Strong experience with Lakehouse architecture using S3, Apache Iceberg, Glue Data Catalog, Redshift
• Strong Python skills for building data processing, evaluation, and automation pipelines, plus familiarity with DevOps practices (CI/CD, infrastructure as code, environment management).
• Good understanding of enterprise data governance and access controls like AWS Lake Formation, Glue Data catalog and metadata management frameworks.
• Good understanding of identity and data security architecture
• IAM, IAM Identity Center, cross account data access patterns, identity propagation for AI agents and services
• Good understanding of AWS infrastructure concepts (networking, security, storage, compute) and how they apply to data and AI workloads.
• Experience working with ETL/ELT pipelines, streaming data, and integration technologies (e.g., CDC, APIs, event buses) for both batch and real-time use cases.
• Proven ability to lead multi-disciplinary teams and influence across platform, AI, data, and business stakeholders.
• Excellent communication and storytelling skills, with the ability to explain complex data/AI architecture decisions in business terms and secure buy-in at VP/SVP levels.

Tech Stack

• Amazon Redshift
• Apache
• AWS
• BigQuery
• Cloud
• ETL
• Postgres
• Python

Benefits

• Best-in-class employee benefits and programs that cater to work-life integration and overall well-being
• Career advancement and upskilling opportunities