Senior Software Engineer, Data Infrastructure (RDBMS) (LATAM)

TRM Labs builds intelligence solutions that help public and private sector agencies investigate and disrupt crime. The Senior Software Engineer will own and scale relational database infrastructure, improving performance, availability, cost efficiency, and operational automation for the company’s data platform.

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, directly determining whether product teams can serve data in real time
• Cut storage and compute costs materially by using AI-assisted analysis (pganalyze paired with Claude) to detect compression and data-model opportunities, then shipping validated changes to production
• Remove team toil by building agentic automation for routine database operations, such as self-serve pgbouncer provisioning, disk scaling, and blue-green deployments, so any engineer can run them safely
• 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 (PeerDB, Fivetran, Debezium) 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 your workflows into AI-native runbooks and internal tooling that teammates reuse

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
• Postgres extension development is a plus