Enterprise Data Architect (Palantir Foundry)

Job Description

Job Description

Enterprise Data Architect (Palantir Foundry)
Preferred location NJ/NY/PA/CT - Jersey City NJ / New York city NY
FTE/Contract
Salary/Pay rate: Market- based on candidate experience

Primary Skills: Data Mesh Architecture implementation using Palantir Foundry
Secondary Skills: Financial Services, Fraud Management platform build experience is a great addition

Design and build the data architecture that fraud detection runs on: the real-time decisioning path, the lakehouse that models are trained from, the data products the estate is organized around, and the virtualization layer that gives investigators context without proliferating copies.

This is a builder's role. The successful candidate will be in design reviews, writing and reviewing technical specifications, and making calls on partitioning, latency budgets and state management. Candidates whose recent experience is exclusively advisory will not be a fit.
## Key responsibilities

• Fraud data architecture- Own the end-to-end design across three latency tiers: the decision plane (sub-150ms, materialised state, no federated access), the investigation plane (seconds, virtualized), and the model and analytics plane (lakehouse)
• Working knowledge of Palantir Data Foundry and Palantir’s Data Mesh implementation experience
• Lakehouse design and build.
• Data mesh implementation - Define the fraud data product topology, the contract standard between producing domains and the fraud platform, the quality metrics attached to each product, and the federated governance model that keeps ownership distributed while evidence stays central
• Data virtualization - Design the federated access layer for investigation and analytics
• Real-time data path - Design the streaming ingestion, the ISO 20022 canonical event model, the bounded-window enrichment join between payment and channel telemetry, and the low-latency feature state store
• Non-functional design - Latency budgets, throughput and capacity modelling, resilience and failure posture, data residency, retention, lineage and encryption