Senior Software Engineer, Data Infrastructure (RDBMS)

TRM Labs

1d•$200,000 - $220,000•Remote

About The Position

TRM Labs is seeking a Senior Software Engineer specializing in Data Infrastructure (RDBMS) to join their Data Platform team. This role involves developing, operating, and scaling relational database systems that handle petabyte-scale data, contributing to TRM's mission of building a safer financial system. The engineer will own the end-to-end serving layer, focusing on query performance, cost optimization, and availability of critical database infrastructure. The position is described as uniquely broad, with significant impact on product teams and the company's ability to serve data in real-time.

Requirements

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
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
A track record of using AI-assisted analysis to drive measurable cost or performance improvements in production database systems

Nice To Haves

Postgres extension development is a plus

Responsibilities

Develop, operate, and scale relational database systems at petabyte scale.
Own the serving layer end to end, from query performance to cost to availability.
Keep customer-facing APIs fast and available at five-nines by owning query tuning, index design, and schema optimization on petabyte-scale Postgres/Citus.
Cut storage and compute costs materially by using AI-assisted analysis to detect compression and data-model opportunities.
Remove team toil by building agentic automation for routine database operations, such as self-serve pgbouncer provisioning, disk scaling, and blue-green deployments.
Protect data integrity and latency by driving agentic validation of database changes before they reach production.
Keep real-time data flowing by managing CDC pipelines and using AI tooling to debug replication failures faster.
Shape the next-generation platform by migrating workloads off first-gen infrastructure and prototyping new data stores with AI-accelerated spikes.
Raise the whole team's leverage by codifying workflows into AI-native runbooks and internal tooling.