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Point72

Data Engineer, Knowledge Graph

New York, NY$200k–$300kmidAdded yesterday

About this role

Point72 seeks a Data Engineer to design and build a financial knowledge graph platform that connects companies, industries, and economic insights. You'll lead technical implementation, drive adoption across the Market Intelligence team, and mentor engineers while developing sophisticated data models and graph processing pipelines.

What you'll do

  • Design and implement financial knowledge graph architecture with complex data models capturing relationships
  • Build graph processing pipelines for data ingestion, transformation, and knowledge extraction
  • Define best practices for knowledge graph development and maintenance across the organization
  • Lead adoption of graph-based solutions and mentor engineering team members
  • Integrate disparate data sources into unified, interconnected knowledge graphs
  • Implement graph algorithms to uncover patterns in financial data

What they're looking for

  • Knowledge graph engineering and graph database development (Neo4j, TigerGraph, Neptune, etc.)
  • Graph data modeling and ontology design for financial applications
  • Advanced Python or similar high-level programming language
  • Data integration and ETL pipeline development
  • Technical leadership and team mentoring
  • Change management and stakeholder collaboration
  • Semantic technologies and knowledge representation

Benefits

  • Fully-paid health care benefits
  • Generous parental and family leave policies
  • 401(k) savings program with employer match
  • Tuition assistance and professional development
  • Mental and physical wellness programs
  • Volunteer opportunities and employee resource groups
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Point72

Point72 operates trading, data infrastructure, and investment management technology platforms that power systematic portfolio management and capital markets operations. The company is hiring infrastructure engineers, data reliability specialists, and software engineers to build and maintain mission-critical systems including data pipelines, network architectures, storage platforms, and research tools.

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Likely interview questions

  • Describe your most complex knowledge graph project—what financial relationships did you model and how did you handle data quality challenges?
  • Tell us about a time you led a team through adopting a new graph technology. How did you drive adoption and overcome resistance?