The impact you will have: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 timeCut 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 productionRemove 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 safelyProtect data integrity and latency by driving agentic validation of database changes before they reach productionKeep real-time data flowing by managing CDC pipelines (PeerDB, Fivetran, Debezium) and using AI tooling to debug replication failures fasterShape the next-generation platform by migrating workloads off first-gen infrastructure and prototyping new data stores with AI-accelerated spikesRaise the whole team''s leverage by codifying your workflows into AI-native runbooks and internal tooling that teammates reuseWhat we''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 analysisHands-on experience with CDC tools (PeerDB, Fivetran, Debezium, Datastream, Airbyte) and comfort using AI tooling to debug replication failure modesFluency with database profiling (pganalyze or equivalent) to interpret metrics and logs, including using LLMs to summarize performance findingsProduction automation experience in Python or Go, including agentic automation of routine database tasks; Postgres extension development is a plusDaily 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 outputA track record of using AI-assisted analysis to drive measurable cost or performance improvements in production database systemsAbout the Team:The Data Platform team is distributed, not distant. Learn about TRM Speed in this position:Within 30 days, eliminate a single point of failure by shipping self-serve scaling automation (pgbouncer provisioning, disk resizing) that any teammate can run, freeing senior engineering timeWithin the first quarter, identify a compression opportunity with AI-assisted analysis, validate it with a proof of concept, and ship it to production for a measurable storage-cost reduction with zero availability impactWithin 90 days, coordinate a schema change across three product-engineering teams in parallel and deliver the platform changes ahead of a product deadline so a customer feature ships on timeThe following represents the expected range of compensation for this role:Individual pay is determined by skills, qualifications, experience, and location.