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-contributor 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 Apache Iceberg specifically.

This search is being conducted exclusively by Talent International (part of the SwipeJobs Group).