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TRM Labs is an AI-powered intelligence platform helping public and private sector agencies investigate and disrupt crime. The Data Platform team builds and operates highly available, scalable data infrastructure serving petabyte-scale data for TRM's products.
As a Senior Software Engineer on Data Infrastructure (RDBMS), you will own the relational database serving layer end-to-end, directly impacting whether product teams can serve data in real time. This is a uniquely broad role at TRM: you own query performance, cost optimization, and availability on infrastructure that product teams depend on daily.
Key responsibilities:
- 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 using AI-assisted analysis (pganalyze paired with Claude) to detect compression and data-model opportunities, then ship validated changes to production
- Remove team toil by building agentic automation for routine database operations (self-serve pgbouncer provisioning, disk scaling, blue-green deployments) so any engineer can run them safely
- Protect data integrity and latency by driving agentic validation of database changes before production
- Keep real-time data flowing by managing CDC pipelines (PeerDB, Fivetran, Debezium) and using AI tooling to debug replication failures
- Shape the next-generation platform by migrating workloads off first-gen infrastructure and prototyping new data stores with AI-accelerated spikes
- Raise team leverage by codifying workflows into AI-native runbooks and internal tooling
The Data Platform team is distributed across US and Canada, operating async-first with intentional synchronous touchpoints. Team culture is product-focused, customer-focused, coaching-oriented, and process-oriented. You will work alongside senior engineers who hold high standards for code quality, operational excellence, and cross-team communication. Collaboration happens primarily async in Slack, Notion, and GitHub; meetings are purposeful and minimal (target: no IC spending more than 3–4 hours per week in meetings). Technical specs and decision docs drive alignment before code.
On-call rotation: approximately once every 15 days; each shift is a 24-hour window across 2 days. (Note: US portion is clear to proceed; Canada portion pending regional compliance review.)
Work week travel: This role requires participation in in-person R&D work weeks, typically 1–2 times per quarter, at a TRM hub or other designated location. Work weeks may be scheduled with as little as 14 days' notice. TRM coordinates and covers approved travel, lodging, and workspace. Accommodations available for caregiving, health, immigration, and similar constraints.
Expected impact milestones:
- Within 30 days: eliminate a single point of failure by shipping self-serve scaling automation
- Within first quarter: identify a compression opportunity with AI-assisted analysis, validate with POC, ship to production for measurable storage-cost reduction
- Within 90 days: coordinate a schema change across three product-engineering teams and deliver platform changes ahead of a product deadline
AI fluency is a baseline expectation at TRM. You will be evaluated on applied AI fluency during the interview process, specifically how you use AI to accelerate repeatable workflows, structure and solve problems, improve output quality, and increase speed and leverage.
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; Postgres extension development is a plus
- Daily use of AI coding tools (Claude, Copilot) to accelerate development and produce higher-quality output faster, with judgment to know when to trust or reject AI output
- Track record of using AI-assisted analysis to drive measurable cost or performance improvements in production database systems
