Note: The job is a remote job and is open to candidates in USA. TRM Labs provides AI-powered intelligence solutions that help public and private sector agencies investigate and disrupt crime. The Engineering Manager will lead the Data Platform team in building highly available, scalable data infrastructure and mission-critical services for analyzing blockchain transaction activity at petabyte scale. The role combines engineering leadership with hands-on oversight of data services, ETL pipelines, data models, database clusters, and cross-functional product development.
Responsibilities
- Lead and nurture a team of high-impact engineers, offering guidance in both technical and cultural aspects
- Provide regular coaching and technical mentorship to your immediate team, while contributing to the broader engineering organization's growth
- Assume direct responsibility for ensuring the quality and timely delivery of the team's work, while overseeing the availability and performance of critical services
- Drive projects aligned with well-defined goals and measurable outcomes, accountable to key performance indicators (KPIs)
- Foster best practices in design and usability, creating scalable solutions while emphasizing documentation and knowledge sharing
- Resolve operational escalations and collaborate with cross-functional teams to address production incidents through resolution
- Collaborate closely with management and product teams to plan, estimate, and prioritize roadmap objectives, while identifying opportunities and mitigating risks
- Contribute to the creation of highly reliable data services that seamlessly integrate with numerous blockchains
- Engineer and optimize ETL pipelines that handle real-time transformation and processing of petabytes of structured and unstructured data
- Architect data models to ensure efficient storage and retrieval, maintaining sub-second latency for querying blockchain data
- Oversee the deployment and monitoring of large-scale database clusters, focusing on performance and high availability
- Collaborate cross-functionally with data scientists, backend engineers, and product managers to design, implement, and refine new data models supporting TRM's products
Skills
- 5+ years managing engineers, including a team of roughly 8–15, with scope that has grown over time (more surface area, more people, or more complex systems than when you started)
- Technical depth you still use. You read design docs closely, ask the question that changes the approach, and can unblock an engineer on the hard part. You don't need to be writing production code daily to stay credible
- A point of view on leadership. You've thought about how you build teams and how you want engineers to work, and you can articulate it. If you've shared it publicly (writing, talks, open source, community), even better
- You disagree well. You've made the case against a roadmap, a design, or your own leadership, brought evidence, and come out of it still in partnership
- Influence beyond your own team. You've delivered work that depended on teams you don't manage (a migration, a shared SLA, a platform dependency) by bringing people along rather than escalating
- Team building as core work. You've hired engineers you're proud of, grown people into senior and lead roles, and treated the quality of the team as part of the product
- Communication that creates momentum. You can frame where a team is heading in a way that makes strong engineers want to build it with you
- A Bachelor's degree (or equivalent) in Computer Science or a related field
- A proven track record with 5+ years of experience conceiving and implementing distributed systems from ideation through production
- Proficiency in Python, along with strong SQL or SparkSQL experience
- Versatility across the full data engineering stack, including:
- Data stores such as ClickHouse, Elasticsearch, Postgres, Redis, and Neo4j
- Data pipeline and workflow orchestration tools such as Airflow, DBT, Luigi, Azkaban, and Storm
- Data processing technologies and streaming workflows, including Spark, Kafka, and Flink
- Infrastructure deployment and monitoring in public cloud environments using Docker, Terraform, Kubernetes, and Datadog
- Demonstrated ability to load, query, and transform large-scale, complex datasets
Benefits
- Distributed-first company with hubs in Los Angeles, San Francisco, New York, Washington D.C., London, and Singapore.
- AI fluency is expected and team members are encouraged to use AI to accelerate and reimagine their work.
- Regular coaching and technical mentorship for the immediate team, while contributing to the broader engineering organization's growth.
- Opportunities to grow and contribute through meaningful, mission-driven work.
Company Overview
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