AI & Data Architect - Greenfield Mandate | VP Level | Alternative Investment Firm

**AI & Data Architect — Greenfield Mandate VP Level Alternative Investment Firm**

Location: Washington, DC or New York, NY (4 days in-office)

Compensation: Highly competitive base + bonus + comprehensive benefits - structured to attract genuinely top-tier talent, with flexibility for the right candidate

Type: Full-time, direct hire

The Opportunity

We’re partnering exclusively with one of the world’s largest alternative investment firms - hundreds of billions in assets under management, operating globally - on a rare, genuine greenfield mandate: architect the firm’s AI and data foundation from a blank page.

This is a senior individual-contributo r role, not a management position. You’ll be the primary technical authority across the firm’s entire data and AI ecosystem, working directly alongside senior data and technology leadership to define how the firm’s data gets built, governed, and made AI-ready for years to come. Real influence, real ownership, no legacy debt to inherit.

What Makes This Role Different

• Greenfield, not inherited
  • you’re defining target-state architecture from scratch, not maintaining someone else’s decisions
• AI at the core, not bolted on
  • semantic layers, retrieval-augmented generation (RAG), data contracts, and agent-facing data foundations are central to the mandate, not a side initiative
• A seat at the table
  • trusted technical advisor to senior leadership on firm-wide data and AI strategy, with real visibility and influence
• Built for someone who can go deep
  • this is a role for someone who doesn’t just implement, but can explain the why behind an architecture decision as clearly as the how

What You’ll Own

• Architect AI-ready data foundations - semantic layers, contextual metadata, data contracts, and retrieval-ready knowledge stores that allow LLMs, agents, and generative AI applications to reason reliably over enterprise data
• Design and govern enterprise patterns for RAG, vector stores, embedding pipelines, and grounding approaches for AI use cases across the firm
• Define target-state architecture for a modern, cloud-native data platform — ingestion, transformation, storage, semantic layers, and consumption
• Establish architectural standards for scalability, performance, security, and cost efficiency across both data and AI workloads
• Partner with data governance functions to extend lineage, quality, and stewardship standards to model inputs, outputs, and AI risk
• Mentor engineers and lead architectural design reviews, influencing technical direction across a matrixed organization without formal authority

Core Stack

Snowflake, dbt, Apache Iceberg, Fivetran, Databricks - genuine, hands-on, production-level depth across this stack is essential. This isn’t a checklist of tools you’ve touched; it’s the foundation of the role.

What We’re Looking For

• 10+ years in data architecture, data engineering, or enterprise analytics
• At least 2 years of direct, hands-on experience architecting generative AI or AI/ML systems in production - not a personal project or proof of concept
• Genuine architectural decision-making depth with Apache Iceberg specifically - able to speak to real tradeoffs (schema and partition evolution, cross-engine interoperability, storage/compute economics), not just familiarity with the tool
• Proven experience designing retrieval, grounding, and semantic layers for LLM- and agent-based applications
• A track record of operating in complex, regulated, or highly governed environments - financial services is a strong plus, but we’re equally interested in candidates from other regulated industries or complex, cutting-edge data environments
• The ability to translate deep technical work into a clear narrative - context, decision, outcome - for both technical and business audiences
• Comfortable operating as a senior individual contributor: setting direction and staying hands-on, rather than managing through others

Why This Role

If you’ve spent your career solving the hard, unglamorous problems underneath the data — governance, lineage, the plumbing that decides whether a model actually works in production — this is a chance to build that foundation at enterprise scale, at a firm where the data itself is the business, not just a reporting layer on top of it.

To apply: please send your resume along with a short note on your hands-on experience with Snowflake, dbt, and A…