Senior Software Engineer, Data Infrastructure; RDBMS

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.

The Data Platform team collaborates with an experienced group of data scientists, engineers, and product managers to build highly available and scalable data infrastructure for TRM's products and services. As a Data Infrastructure Engineer, you will be responsible for developing, managing, and scaling TRM’s robust database infrastructure that will ultimately help to build a safer financial system for billions of people.

The impact you will have here:

• Design and maintain petabyte scale high-performance databases and data models that support real-time investigations and analytics use cases
• Build and optimize production data pipelines—batch and streaming—that transform large-scale blockchain datasets
• Diagnose and tune complex SQL queries under heavy load, working closely with product and research teams
• Own key infrastructure initiatives—from query optimization and index strategy to storage optimization and system resilience
• Collaborate cross-functionally to deliver reliable and impactful data workflows end-to-end

What we’re looking for:

• 5+ years of experience in data engineering, analytics infrastructure, or backend systems with RDBMS depth
• Experience implementing and maintaining database security measures, including access control, encryption, and compliance with security frameworks and standards
• Proven expertise with at least one of:
  Postgres, MySQL, or SQL Server at production scale (e.g., TB-scale datasets, concurrency, replication, tuning)
• Strong command of SQL reasoning—you know how to debug, explain, and optimize queries, not just write them
• Experience designing and evolving data models (normalized and denormalized) to support analytical or operational use cases
• Familiarity with data pipeline frameworks (e.g., Airflow, DBT, custom orchestration)
• Systems thinking and ownership mindset—you’re comfortable solving ambiguous, cross-functional problems from end to end

About the Team:

• The Data Platform team is the funnel between all of TRM's data world and product world. We care about all layers of stack including petabyte of data stores, pipelines, and processes.
• We have quite a big scope as a the team with new and exciting projects every quarter. As a result, we collaborate across the board with most teams at TRM.
• We believe in async communication and are also not afraid to jump on a quick huddle if that helps to move things faster. We are both scrappy when the situation demands and also process-oriented when we need to achieve our OKRs.
• We are always looking for people who can elevate the quality our tech and our execution. If you enjoy a remote-first and async friendly environment to achieve efficacy and efficiency at petabyte scale, our team could be a great pick for you!
• Team members are based in the US across almost all timezones! Our on-call tends to be in EST/PST shift, whatever suits you the best.
• We do try to reserve some overlap in the day for meetings. Our north star - no IC spends more than 3-4 hours/week in meetings.

Learn about TRM Speed in this position:

• Buildscalable engines to optimize routine scaling and maintenance tasks like create self-serve automation for creating new pgbouncer, scaling disks, scaling/updating of clusters, etc.
• Enable tasks to be faster next time and reducing dependency on a single person.
• Identify ways to compress timelines using80/20 principle. For instance, what does it take to be operational in a new environment? Identify the must have and nice to haves that are need to deploy our stack to be fully operation. Focus on must haves first to get us operational and then use…