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.
Key Responsibilities
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
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 authority
Comfortable in a high-growth environment where the right answer for 50 engineers isn't the right answer for 100
Pragmatic outlook during incidents — focused on preventing the next one
Benefits
Full-Time US Employee Benefits Include
Some of the nicest and smartest teammates you’ll ever work with
