Senior Data Engineer (Graph Analytics)

Senior Data Engineer, Graph Analytics

Company

TRM Labs

Conditions

1 day agoSenior North America Remote Full Time Data Science Jobs by TRM Labs

Skills

Distributed Database Hadoop Graph Algorithm Graph Graph Theory Algorithm Design Data Engineering Dask Sql Python Apache Spark Blockchain Communication Data Structure Apache Hadoop Distributed Processing

About the Role

You will build scalable graph systems that analyze large networks of cryptocurrency transactions. You will design and implement graph algorithms that operate at multi-blockchain scale and optimize them for performance. You will leverage distributed databases and graph processors to implement real-time graph algorithms and integrate diverse data inputs. You will collaborate with data scientists, engineers, and investigators to convert research into production features, communicate complex implementation details to technical and non-technical audiences, and refine risk models that assign scores to addresses.

Requirements

• Academic background in a quantitative field such as Computer Science, Mathematics, Engineering, or Physics
• Strong knowledge of algorithm design and data structures
• Experience optimizing large-scale distributed data processing systems such as Apache Spark, Apache Hadoop, Dask, and distributed graph databases
• Experience converting academic research into production products
• Strong programming experience with Python and SQL
• Excellent communication skills for technical and non-technical audiences
• Self-motivated with ability to propose and validate solutions with minimal guidance
• Knowledgeable of basic graph theory concepts

Responsibilities

• Design and implement graph algorithms that analyze large cryptocurrency transaction networks
• Research graph-native technology and evaluate its benefit to data teams
• Collaborate with investigators, data scientists, and engineers to define requirements and user stories
• Understand and refine risk models that assign scores to addresses
• Communicate complex implementation details to technical and non-technical audiences
• Integrate diverse data inputs from raw blockchain data to model outputs
• Leverage distributed databases and graph processors to implement real-time graph algorithms

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