Senior Data Engineer, Palantir

Who We Are

Cross River is a highly profitable, fast-growing financial technology company powering the future of financial services. Our comprehensive suite of innovative and scalable embedded payments, cards, and lending products deliver financial services for millions of businesses and consumers around the globe. Cross River is backed by leading investors and serves the world’s most essential fintech and technology companies. Together with its partners, Cross River is reshaping global finance and financial inclusion. 

We are on a mission to build the infrastructure that propels access, inclusion, and the democratization of financial services. While our company has tripled in size over the last three years, our strong sense of purpose led Cross River to be named to American Banker’s list of Best Places to Work in Fintech for the last 6 years. The reason for this success is simple – our nimble and collaborative family culture lives in every member of our growing team. Together we are at the forefront of technology and innovation, and we invite passionate, collaborative, and motivated high performers to join our expanding team.

About Our Team

Cross River’s team is made up of problem solvers hungry to build and perfect new products and systems. We work with team members in most US time zones. Although we are in separate places, we still make space to know one another and have fun! We collaborate, help and mentor each other, and check in on our progress and blocks frequently. We work together primarily with Confluence, GitHub, JIRA, and Teams.

What We’re Looking For

We are seeking a highly skilled and motivated Senior Palantir Data Engineer to support and enhance our AML (Anti-Money Laundering) & Fraud development capabilities within Palantir Foundry. This role will focus on implementing, maintaining, testing and tuning rule-based and data-driven AML detection pipelines. You will work closely with compliance analysts, data modelers, and platform engineers to operationalize AML logic using Palantir's object-based, scalable architecture.

Role and Responsibilities:

1. AML & Fraud Scenario Implementatoin
   • Collaborate with Compliance and Product teams to implement, and maintain AML monitoring scenarios in Foundry using Code Workbooks, Object Builders, and Pipeline Workflows.
   • Implement business provided technical detection logic, supporting both rule-based and advanced data-driven use cases.
2. Data Pipelines Design & Transformation
   • Work within Foundry's Ontology framework to model key AML entities such as customers, transactions, accounts, and alerts.
   • Develop and maintain pipelines for data enrichment, feature engineering, and temporal analysis of financial behavior.
3. Testing & Quality Assurance
   • Create test cases to validate correct implementation of scenario logic, pipeline outputs, and transformations.
   • Work with QA and Compliance stakeholders on implementation quality testing including accuracy, edge cases, and scenario coverage.
4. Monitoring & Performance Optimization
   • Monitor pipeline execution, resolve data processing issues, and optimize Spark-based workflows for performance and reliability.

1. Stakeholder Engagement & Documentation
   • Maintain clear, version-controlled documentation as it relates to implementation of AML scenarios and associated data flows.
   • Work cross-functionally with Investigations, BSA/AML Compliance, and Data Governance teams to ensure alignment with regulatory and operational standards.
2. Platform Support
   • Serve as L2 support for AML scenario pipelines and address issues raised by analysts or engineering operations.
   • Participate in platform enhancement efforts related to AML & Anti-Fraud implementation, rule authoring frameworks, and investigation tooling.

Required Skills & Experience:

• Palantir Foundry Expertise: Hands-on experience developing within Foundry using Pipelines, Code Workbooks (Python, PySpark, SQL), Object Builder, and Ontology.
• Pipeline Development: Proven ability to build, maintain, and optimize data pipelines in a governed, modular data environment.
• Testing & CI/CD Practices: Experience with unit testing, result validation, and deployment processes within enterprise data platforms.
• Big Data Processing: Strong experience with Spark, PySpark, and SQL for large-scale data transformation and analysis.
• Collaboration: Excellent communication skill