AI Data Platform Architect - Private Markets

# Principal AI Data Platform Architect ## Company Overview We are a fast-growing AI-native firm working with executives, operators, private-markets investors, and enterprise teams to redesign how mission-critical work gets done with AI. We move quickly, care deeply about execution quality, and build practical systems where data, workflows, and AI agents come together in production. Our work often sits inside complex enterprise environments with sensitive private data, messy documents, high-stakes decisions, and strict access controls. We are not building generic dashboards or chatbots. We are building governed operating systems that help people answer important business questions faster, with source trails and permission boundaries intact. ## Opportunity We are looking for a principal-level Data Platform Architect to design and build the governed data spine behind AI-native operating systems for private-markets and enterprise environments. This role is for a senior, hands-on builder who can architect the foundation and ship production-grade systems: ingestion, lakehouse layers, canonical entities, lineage, quality checks, permissions, semantic models, and serving APIs. Outstanding performers may be considered for expanded or longer-term opportunities, including deeper platform ownership. ## Scope of Work - Architect a lakehouse-style data platform across structured and unstructured enterprise sources. - Build ingestion pipelines from SharePoint, Microsoft 365, CRM systems, document repositories, spreadsheets, and financial or operational data feeds. - Design Bronze/Silver/Gold data layers with replayability, lineage, quality checks, and point-in-time correctness. - Create canonical entity models for companies, people, deals, documents, metrics, funds, assets, and relationships. - Implement role-based and attribute-level access controls at the data layer, not just the UI. - Build semantic models and APIs that downstream AI workflows can safely query. - Partner with AI engineers building RAG, extraction agents, and executive command surfaces. - Document architecture, tradeoffs, operating standards, and handoff paths clearly. ## Must-Haves - Expert Python, SQL, and modern data engineering. - Deep experience with Databricks, Snowflake, or comparable lakehouse/data-platform architecture. - Hands-on experience with dbt or comparable transformation frameworks. - Experience building governed enterprise data systems with lineage, quality tests, CI/CD, and observability. - Familiarity with Microsoft Graph, SharePoint, Microsoft 365, or similar enterprise content ingestion. - Experience with entity resolution, master data management, semantic layers, or canonical data modeling. - Strong judgment around sensitive data, access controls, auditability, and reliability. - Ability to personally architect and ship production systems, not just advise. ## Nice-to-Haves - Private equity, private markets, financial services, investment workflows, or enterprise knowledge-management data experience. - Experience with DealCloud, HubSpot, PitchBook, AlphaSense, S&P, fund admin feeds, or similar business-data sources. - Experience with graph databases, vector databases, or RAG-ready data architecture. - Azure, Entra ID, RBAC, row-level security, or regulated-data environments. - Experience turning a client-specific data platform into reusable product infrastructure. ## What We're Looking For in a Person We are looking for a serious enterprise data architect who cares about correctness, lineage, permissions, and reliability. The right person has built real systems with messy data and real users. They know that the hard part is not making a demo work; it is making the data trustworthy, traceable, secure, and useful every day. This person should be senior enough to challenge the architecture, hands-on enough to ship, and clear enough to explain technical tradeoffs to non-technical operators. ## Category **Data Science & Analytics - Data Engineering** ## Screening Questions 1. Describe a governed data platform, lakehouse, or enterprise data architecture you personally designed or built. What were the sources, layers, and serving use cases? 2. What is your hands-on experience with Databricks, Snowflake, Delta Lake, Iceberg, or comparable platforms? 3. Have you built entity-resolution, master-data, or canonical data-model systems? Describe the matching approach and human-review process. --- ## Skills - Data Engineering - Data Architecture - Databricks - Snowflake - SQL - Python - dbt - ETL Pipeline - Data Lake - Microsoft Azure - API Integration - Data Modeling - Data Warehousing - Lakehouse Architecture - Microsoft Graph - SharePoint Integration - Entity Resolution - Master Data Management - Semantic Layer - Data Lineage - RBAC - Private Equity Data