Senior Database Reliability Engineer

About Scribe

Scribe is where exceptional people come to do the best work of their careers. More than 94% of the Fortune 500 use Scribe to own their specialized intelligence: the unique way their teams work, decide, and get things done. Our Specialized Intelligence platform automatically captures how work happens and turns it into a living asset for people and AI agents.

We're growing fast. Since our founding in 2019, we've reached 7 million users across 600,000 businesses. Based in San Francisco, we're a LinkedIn Top Startup, valued at over $1B, and backed by leading investors. Join us in our mission to transform how people work.

About the role

We're hiring a Senior Database Reliability Engineer to own the reliability, performance, and scalability of Scribe's data tier. Our engineering org is doubling — which means the guardrails, automation, and standards you put in place today will carry a much larger team through the next phase of growth. This is a senior IC role with real ownership: you'll set the bar for how engineers across the company interact with our databases, not just keep the lights on.

Our stack is Django on PostgreSQL (Aurora Serverless V2), OpenSearch, Redis (ElastiCache), SQS, and RabbitMQ, with a CDC pipeline running Aurora to DMS to S3 Parquet to Snowflake. Engineers ship through the ORM, not raw SQL — which makes migration safety, index design, and query review genuinely high-stakes work.

What you'll do

You'll be a key engineer on the data tier - helping design schemas and access patterns that scale, tune Aurora for latency and throughput, and reinforce 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, you're shipping the fix.

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

Apply and extend conventions for QuerySet usage and physical schema design (indexes, constraints, partitioning)

Help scale review through automation — contribute to AGENTS.md files and DNA scaffolding that encode our conventions, tune 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

Contribute to major infrastructure initiatives

Capacity planning workstreams 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

Operate and extend the CDC pipeline (Aurora → DMS → S3 Parquet → Snowflake)

DMS task tuning, replication slot hygiene on the Postgres side

Schema evolution as Django migrations roll through — so a column drop doesn't silently break the warehouse at 6 AM

Parquet layout and partitioning, reliability of the Snowflake handoff

Build automated checks that flag migrations likely to break downstream consumers

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

Mentor and share knowledge — With a large incoming class of engineers, contribute to onboarding docs, tech talks, and written guidance — which double as input for the AI review tooling. Run sessions on "what your ORM query is really doing." Participate in incident response and blameless postmortems for the data tier.