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TRM Labs is an AI-powered intelligence platform helping public and private sector agencies investigate and disrupt crime by tracing illicit activity and building threat network operating pictures. The Data Platform team builds highly available, scalable data infrastructure that processes blockchain transaction activity at petabyte scale.
In this role, you will own mission-critical systems and data services that analyze blockchain data in real-time. You'll design and build highly reliable data services integrating with dozens of blockchains, develop complex ETL pipelines transforming petabytes of structured and unstructured data, architect intricate data models for sub-second latency querying, and oversee deployment and monitoring of large database clusters with focus on performance and high availability. You'll collaborate across data scientists, backend engineers, and product managers to design novel data models enhancing TRM's products.
TRM operates with high velocity and ownership, shipping in days what takes months elsewhere. The environment requires comfort with ambiguity, quick priority changes, and intense collaboration. You'll be expected to identify optimization opportunities using the 80/20 principle, build self-serve automation to reduce manual scaling tasks, and deliver "skateboard" versions of projects to get operational quickly before hardening for production.
TRM is Series C with $220M funding, backed by Goldman Sachs, Bessemer, Y Combinator, and Thoma Bravo. Headquartered in San Francisco with distributed hubs in LA, NYC, DC, London, and Singapore. The company emphasizes AI fluency as a baseline expectation—you should be in the top 10% of operators in your function applying AI to accelerate workflows, structure problems, improve quality, and increase speed.
REQUIREMENTS:
- Bachelor's degree (or equivalent) in Computer Science or related field
- 5+ years hands-on experience architecting distributed systems from ideation through production deployment
- Exceptional programming skills in Python; adeptness in SQL or SparkSQL
- Versatility across data engineering spectrum, including:
- In-depth experience with data stores/engines (Iceberg, Trino, BigQuery, StarRocks, Citus)
- Proficiency in pipeline/workflow orchestration (Airflow, DBT)
- Expertise in data processing and streaming (Spark, Kafka, Flink)
- Competence deploying/monitoring infrastructure on public cloud (Docker, Terraform, Kubernetes, Datadog)
- Proven ability loading, querying, and transforming extensive datasets
