Senior Software Engineer, Data Infrastructure; RDBMS
Listed on 2026-06-04
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Software Development
Data Engineer
Senior Software Engineer, Data Infrastructure (RDBMS)
United States
Build to Protect CivilizationTRM is a blockchain intelligence company that’s on a mission to build a safer financial system for billions of people. We’re a lean, high‑impact team tackling some of the world’s most critical challenges, ranging from human trafficking and financial fraud to terrorist financing. We are builders who power governments, financial institutions, and crypto companies when the clock is running and the consequences are real.
This is why every TRMer is a bet on our future and has the power to change our trajectory.
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.
Theimpact 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
- 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
- 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 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 of 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.
- Build scalable 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 using 80/20 principle. For instance, what does it take to be operational in a new environment? Identify the must‑have and nice‑to‑have that are needed to deploy our stack to be fully operational. Focus on must‑haves first to get us operational and…
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