# Senior Software Engineer, Data Infrastructure (RDBMS) at TRM Labs

- Company: TRM Labs
- What the company does: Trusted by public sector agencies and private sector institutions on the front lines of illicit activity, including criminal networks, sanctioned actors and state-backed threats. Backed by Bessemer and Y Combinator.
- Company website: https://www.trmlabs.com/
- Type: Startups (AI role)
- Level: Senior
- Location: North America
- Work setup: Remote
- Pay: $200K to $220K base salary per year (USD)
- Posted: 2026-09-22
- Apply by: 2026-11-06
- Apply: https://jobs.ashbyhq.com/trm-labs/33af3036-a865-44cd-a609-76f91d3c203d
- Page: https://www.1752.vc/careers/jobs/trm-labs-senior-software-engineer-data-infrastructure-rdbms/

## About the role

The Data Platform team builds and owns highly available, scalable data infrastructure for TRM's products and services. As a Senior Software Engineer on Data Infrastructure (RDBMS), you will develop, operate, and scale the relational database systems that serve data at petabyte scale, helping build a safer financial system for billions of people.

## What they're looking for

- 5–8 years building and operating production PostgreSQL (Citus, Aurora, AlloyDB, or equivalent distributed Postgres)
- Deep SQL optimization skills (Explain Plans, CTEs, window functions, partitioning, index design, query-planner behavior in distributed environments), increasingly paired with AI-assisted query analysis
- Hands-on experience with CDC tools (PeerDB, Fivetran, Debezium, Datastream, Airbyte) and comfort using AI tooling to debug replication failure modes
- Fluency with database profiling (pganalyze or equivalent) to interpret metrics and logs, including using LLMs to summarize performance findings
- Production automation experience in Python or Go, including agentic automation of routine database tasks, Postgres extension development is a plus
- Daily use of AI coding tools (Claude, Copilot) to accelerate development and produce higher-quality output faster, with the judgment to know when to trust or reject AI output

Tags: R&D