Job description

About Scribe Scribe scribe.com 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. What we're looking for - Strong PostgreSQL — EXPLAIN (ANALYZE, BUFFERS), MVCC, bloat, lock contention, vacuum/autovacuum. Aurora Serverless V2 experience preferred (storage model, reader/writer split, ACU scaling) - 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, column projection, aggregations, and subqueries - Single-region multi-AZ design — practical understanding of what it does and doesn't protect against - 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) and CloudWatch (custom metrics, alarms, log insights) - Experience using AI coding and review tools in a team setting — writing or maintaining AGENTS.md files, configuring review agents, iterating on prompts and configs - At least one production message broker (SQS, RabbitMQ, Kafka) — delivery semantics, idempotency, failure modes - Solid automation and IaC background — real code (Python, Go, or similar) and Terraform - Track record contributing to cross-team initiatives, writing design docs that hold up, collaborating effectively across teams - Comfortable in a high-growth environment where conventions evolve quickly 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 Why you'll love working here - Hard, open problems. Nobody hands you a finished spec. You'll decide what to build with your PM and designer, then own how it performs. - The reach of a unicorn, the headcount of a startup. We went from $1M to $100M ARR in under four years, with fewer than 200 people. That means more pie than people: scope grows with your impact. Customers like T-Mobile, LinkedIn, HubSpot, New York Life and Northern Trust run on Scribe, so what you own reaches some of the biggest names in business. - Smart is the floor. Kind is the bar. Low ego, high standards. We compete as a collective and love to see each other win, and One Team, One Dream is one of the most shouted-out values in #values. Compensation & Location San Francisco (hybrid, 3 days per week in-office) or, Remote based permanently in PST (Pacific Standard Time). All full-time employees receive equity in Scribe. Final offers depend on experience and scope. Benefits - Healthcare Coverage – Comprehensive health, dental, and vision plans, including two $0/month medical plan options for employees - Equity – Ownership in what we're building - 401(k) Plan – through Vestwell - Flex Benefit – $500/year for home office equipment, productivity tools, learning & development or fitness/wellness - Commuter Benefits – $100/month for SF based employees - PTO – Flexible paid time off and company holidays - Parental Leave – Paid leave to support growing families - Wellness & Family Support – Free Talkspace membership, One Medical access (location-dependent), and Kindbody discounts for family planning At Scribe, we celebrate our differences and are committed to creating a workplace where all employees feel supported and empowered to do their best work. Scribe is proud to be an Equal Opportunity Employer.

Requirements

Must-haves

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)
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
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:

Nice-to-haves

Strong PostgreSQL — EXPLAIN (ANALYZE, BUFFERS), MVCC, bloat, lock contention, vacuum/autovacuum. Aurora Serverless V2 experience preferred (storage model, reader/writer split, ACU scaling)
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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlpostgresrestawspythongo

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Paid Time Off

Parental Leave

Wellness