Senior Data Engineer, Graph Analytics

TRM Labs provides blockchain analytics and AI solutions to enhance safety and security in financial transactions. The Senior Data Engineer will design and implement scalable graph algorithms to analyze cryptocurrency transaction networks and collaborate with cross-functional teams to refine risk models and integrate diverse data inputs.

Responsibilities:

• Designing and implementing graph algorithms that analyze large cryptocurrency transaction networks at multi-blockchain scale
• Researching new graph-native technology to evaluate benefit to data science and data engineering teams at TRM
• Working on a highly cross-functional team that collaborates with cryptocurrency investigators to identify key user stories and requirements for new graph algorithms and features
• Understanding and refining TRM’s risk models which analyze large networks of cryptocurrency transactions to assign risk scores to addresses
• Communicating complex implementation details to a variety of audiences from investigators and customer success stakeholders to data engineers and data scientists
• Integrating with a diverse set of data inputs ranging from raw blockchain data to complex model outputs

Requirements:

• Your academic background is in a quantitative field such as Computer Science, Mathematics, Engineering, or Physics
• You have strong knowledge of algorithm design and data structures, and have experience applying this knowledge towards real-world problems
• You have experience optimizing large-scale distributed data processing systems such as Apache Spark, Apache Hadoop, Dask, and distributed graph databases
• You have experience converting academic research into products and have worked with research teams that regularly ship new features
• You have strong programming experience with Python and SQL
• You are an excellent communicator who is skilled at tailoring explanations of complex topics to both technical and non-technical audiences
• You are self-motivated. You propose and validate solutions with minimal guidance. You're comfortable working with ambiguity and shaping your own research direction while being accountable for outcomes
• You are knowledgeable of basic graph theory concepts