Staff Database Reliability Engineer

About the role

We're hiring a Staff Database Reliability Engineer to own the strategy, architecture, and operational excellence of our data infrastructure. This is an expert-level IC role with deep influence on engineering direction, partnering closely with platform, backend, and DevOps engineers. Why this role matters You will own the data tier end-to-end. Design schemas and access patterns that scale, tune Aurora for latency and throughput, and set the standards for how engineers interact with our databases. When a migration script seizes up mid-deploy and writes start queueing behind an ACCESS EXCLUSIVE lock, your runbooks and automation resolve the incident quickly. Make the Django ORM a strength, not a liability: Review migrations for safety at scale - locks, backfills, concurrent index builds, NOT VALID constraints Catch N+1 patterns and missing select_related / prefetch_related in review Establish conventions for QuerySet usage and physical schema design (indexes, constraints, partitioning) Scale review through automation, not heroics - author AGENTS.md files and DNA scaffolding that encode our conventions, configure AI review bots (Claude Code, Cursor, etc.) to flag risky migrations and ORM anti-patterns, and iterate on those configs as new failure modes emerge Lead major infrastructure initiatives: Capacity planning as traffic and engineering throughput grow Zero-downtime schema migrations and cutovers Multi-AZ resilience within a single region - Aurora writer/reader placement, failover behavior and RTO/RPO, ElastiCache and OpenSearch AZ topology, RabbitMQ survivability across AZs Backups, PITR, failover testing, retention Own the CDC pipeline (Aurora → DMS → S3 Parquet → Snowflake): DMS task design and tuning, replication slot hygiene on the Postgres side Schema evolution as Django migrations roll through - so a column rename doesn't silently break the warehouse at 6 AM Parquet layout and partitioning, reliability of the Snowflake handoff Automated checks that flag migrations likely to break downstream consumers Drive observability across three complementary tools: pganalyze - query-level performance, index advisor, schema insights - the go-to for "why is this ORM query slow" CloudWatch - infrastructure metrics and alarms for Aurora, OpenSearch, ElastiCache, SQS, DMS Honeycomb - high-cardinality tracing that ties slow DB calls back to users, flags, deploys, and flows Shape how the three fit together, including Django-side instrumentation and trace attributes on ORM queries Build tooling and guardrails: Migration review automation and CI checks for risky patterns Slow query pipelines fed from pganalyze Self-service dashboards so teams understand their own query footprint Support and evolve the rest of the stack: OpenSearch - index design, sharding, mapping changes, reindexing strategy, Django-side indexing pipelines Redis - caching patterns, eviction, sizing, Django cache framework, Celery/RQ usage, avoiding hot keys and thundering herds SQS + RabbitMQ - queue design, DLQs, visibility timeouts, exchange/queue topology, AZ mirroring, consumer backpressure, Celery behavior under load What makes you a great fit Core expertise: Deep PostgreSQL - EXPLAIN (ANALYZE, BUFFERS), MVCC, bloat, lock contention, vacuum/autovacuum. Aurora Serverless V2 / Limitless experience strongly preferred (storage model, reader/writer split, ACU scaling) Strong ORM fluency (Django, SQLAlchemy, ActiveRecord, or similar) - predict the SQL a query will generate, spot N+1 problems on sight and how to control eager loading (joins vs. batched IN queries), column projection, aggregations, and subqueries Single-region multi-AZ design - practical understanding of what it does and doesn't protect against Data movement and observability: Production CDC experience, ideally AWS DMS - comfortable with logical replication, slot hygiene, schema evolution, and Parquet-based data lakes feeding Snowflake (or BigQuery/Redshift) Hands-on with pganalyze (or Datadog DBM / Performance Insights / pg_stat_statements pipelines), CloudWatch (custom metrics, composite alarms, log insights), and Honeycomb (or another high-cardinality tracing tool) - comfortable with OpenTelemetry and opinionated about what makes a trace useful AI-assisted workflow: Real experience making AI coding and review tools useful for a team - writing AGENTS.md files, configuring review agents, versioning and iterating on prompts and configs The rest of the stack: OpenSearch at scale - sizing, sharding, JVM tuning, rolling upgrades, snapshots Production Redis - persistence tradeoffs, cluster mode, hot keys, thundering herds At least one production message broker (SQS, RabbitMQ, Kafka) - delivery semantics, idempotency, failure modes Engineering and leadership: Strong automation and IaC background - real code (Python, Go, or similar) and Terraform Track record leading cross-team initiatives, writing design docs that hold up, influencing without auth

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