Senior Database Reliability Engineer

What You'll Do

• Own database reliability across Aurora, OpenSearch, Redis, and our CDC pipeline — including schema design reviews, migration safety (locks, backfills, concurrent index builds, NOT VALID constraints), and incident response for the data tier
• Make the Django ORM a strength at scale: catch N+1 patterns in review, extend QuerySet conventions and physical schema standards, and build the CI checks and AGENTS.md scaffolding that encode those standards so they scale beyond any single reviewer
• Operate and evolve the CDC pipeline from Aurora through DMS to S3 Parquet to Snowflake – including replication slot hygiene, schema evolution safety, and automated checks that catch migrations likely to break downstream consumers before they ship
• Build and improve observability across pganalyze, CloudWatch, and Honeycomb, with Django-side instrumentation that ties slow ORM queries back to specific users, flags, and deploys
• Drive multi-AZ resilience within our single-region architecture — Aurora writer/reader placement, failover behavior, RTO/RPO, ElastiCache and OpenSearch AZ topology, RabbitMQ survivability
• Build self-service tooling and dashboards that give product and platform teams visibility into their own query footprint, reducing the review burden as the engineering org grows
• Contribute to onboarding and knowledge-sharing as a large incoming class of engineers joins — write docs, run internal sessions on "what your ORM query is really doing," and feed that knowledge back into AI review tooling

What We're Looking For

• Has deep PostgreSQL expertise in practice: reads EXPLAIN (ANALYZE, BUFFERS) fluently, understands MVCC, bloat, lock contention, and vacuum behavior, and can tune Aurora Serverless V2 for latency and throughput
• Has worked with an ORM (Django, SQLAlchemy, ActiveRecord, or similar) at production scale – can predict the SQL a query generates, spot N+1 issues on sight, and knows when joins beat batched IN queries and when they don't
• Has run CDC pipelines in production, ideally with AWS DMS — comfortable with logical replication, slot hygiene, schema evolution, and Parquet-based data lakes feeding Snowflake, BigQuery, or Redshift
• Has hands-on experience with pganalyze (or Datadog DBM / pg_stat_statements pipelines), CloudWatch, and Honeycomb (or another high-cardinality tracing tool); comfortable with OpenTelemetry
• Has worked with OpenSearch, Redis, and at least one production message broker (SQS, RabbitMQ, or Kafka) at scale
• Writes real automation — Python, Go, or similar — and has used Terraform or comparable IaC to manage infrastructure
• Has used AI coding and review tools in a team setting: written or maintained AGENTS.md files, configured review agents, iterated on prompts

Nice to Have

• Event sourcing on Postgres, or experience with alternate CDC tooling (Debezium, Fivetran, Airbyte)
• pgbouncer or RDS Proxy at scale with Django connection handling
• Deep Honeycomb usage: SLOs, BubbleUp, Triggers, derived columns
• Snowflake from the producer side: staging, Snowpipe, external tables on Parquet
• Experience scaling data infrastructure through rapid engineering headcount growth
• SOC 2 Type II, GDPR, or similar compliance work