Senior Data Engineer, Graph Analytics
TRM LabsRemotePosted 21 February 2026
Job Description
Build a Safer World.
TRM Labs provides blockchain analytics and AI solutions to help law enforcement and national security agencies, financial institutions, and cryptocurrency businesses detect, investigate, and disrupt crypto-related fraud and financial crime. TRM’s blockchain intelligence and AI platforms include solutions to trace the source and destination of funds, identify illicit activity, build cases, and construct an operating picture of threats. TRM is trusted by leading agencies and businesses worldwide who rely on TRM to enable a safer, more secure world for all.
As a Senior Data Engineer on the Graph Analytics team, you will build scalable graph systems that analyze large networks of cryptocurrency transactions. You will collaborate closely with engineers, data scientists, and investigators to design mission-critical graph algorithms that analyze flows of funds. You will leverage distributed databases and graph processors to implement real-time graph algorithms at the multi-blockchain scale. You will collaborate with data science teams to identify opportunities to apply tools and techniques from graph theory to a variety of predictive learning problems.
The impact you will have:
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
What we’re looking for:
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.
About the Team:
Our team is spread out over several countries and time zones. To leverage the opportunities and address challenges that come with this working model, we've built a culture grounded in trust, transparency, and adaptability. Even though we're scattered across different time zones, we stay closely connected through open communication and regular check-ins. Our use of digital tools helps us collaborate seamlessly and work at an impressive pace, delivering results at TRM speed while staying in sync. The diversity of perspectives and expertise within our team sparks creative exchanges and enhances our problem-solving capabilities, allowing us to leverage our global synergy to drive innovation and efficiency.
We're driven by a deep sense of ownership, constant pursuit of improvement and an obsession with delivering magical experiences to our customers; these are a core part of our culture. We're driven ... (truncated, view full listing at source)
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