Engineering Manager, Data
Blockchain intelligence company providing tools to detect, investigate, and manage crypto-related fraud, financial crime, and compliance for institutions and government agencies.
Maintainer signals as of 8/23/2026
Funding history
Investors
About TRM Labs
TRM Labs provides blockchain intelligence for investigations and compliance, offering products such as forensics, wallet screening, entity screening, transaction monitoring, and APIs. It serves financial institutions, crypto businesses, and public sector agencies to trace funds, assess risk, and build cases across digital assets.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Own and evolve the platform supporting more than 1 petabyte of blockchain intelligence products, including batch processing, real-time systems, and 24/7 APIs. Lead two sub-teams, set technical direction, improve delivery speed, and enable data scientists to build and ship intelligence products efficiently.
Requirements
- 8+ years of hands-on data or engineering experience and 4+ years of direct people management.
- Experience as a player-coach reviewing architecture, debugging pipelines, and making system design decisions while setting team strategy.
- Experience owning production systems at scale, such as customer-facing data products, APIs, or decisioning engines.
- Strong Python and SQL skills, with experience in BigQuery, Airflow, data lake architecture, and serving layers.
- Active daily use of AI tools to accelerate development and challenge assumptions.
- Good judgment when making technology and platform investment decisions.
Responsibilities
- Own the platform supporting 1+ petabyte of intelligence products, including batch processing, real-time systems, and 24/7 APIs.
- Build foundational AI architecture for blockchain intelligence infrastructure.
- Drive a 10x improvement in the speed of delivering intelligence models and data products to customers.
- Lead and hire for two sub-teams spanning data infrastructure and product-facing delivery.
- Set technical direction and make architecture, tooling, and platform investment decisions.
- Build platforms that accelerate data science workflows.
