Staff Data Platform Engineer

About the role

ABOUT INSCRIBE

At Inscribe, we're on a mission to catch fraud with confidence. In 2017, we set out to build the first document fraud detection product. Today, we process millions of applications each year and work with many of the leading fintechs and banks across the U.S. We believe the game of cat-and-mouse in fraud requires a fundamentally new approach. With care for our customers, attention to detail, and grounded execution, we’re reimagining how fraud is detected and prevented.

Inscribe is trusted by leading banks and fintechs, including Coinbase, Plaid, Bluevine, and Ramp. Inscribe is backed by Threshold Ventures, Uncork Capital, Y Combinator, Crosslink Capital, and Foundry, and is headquartered in San Francisco, CA.

The Opportunity Inscribe is scaling quickly, and our platform sits at the heart of our customers’ fraud workflows. Every document we analyse and every real-time API decision depends on datastores that are fast, available, and built to grow. We’re hiring a Staff Data Platform Engineer to own how we run, scale, and deploy our databases in the cloud.

This role blends database engineering, site reliability, cloud architecture, and platform automation. You’ll own the reliability and performance of our data layer, from query plans and indexing to replication, failover, backups, and cost. You’ll make sure that as customer volumes and data grow, our datastores stay fast, stable, and cost-efficient.

We’re looking for a high-agency engineer who can assess our data infrastructure today, find what won’t scale, and fix it in line with business goals. This is a hands-on role for someone who thinks in systems, treats databases as a product for other engineers, and raises the bar for data reliability across the company.

Responsibilities

- Own the design, operation, and scaling of our datastores (Postgres, Redis) on AWS, including provisioning, configuration, replication, high availability, and disaster recovery.

- Lead datastore optimization: query and index tuning, schema design, connection pooling, caching strategies, and maintenance work such as vacuuming, bloat management, and partitioning.

- Plan and carry out database scaling as data volumes grow, using read replicas, partitioning, archival, sharding, and horizontal scaling where needed, ahead of demand rather than in response to incidents.

- Define and run database reliability practices: SLOs for the data layer, backup and point-in-time recovery testing, failover drills, defined RPO/RTO targets, and incident runbooks.

- Optimize deployment pipelines, test environments, and release automation to improve developer efficiency and reduce operational risk, including safe zero-downtime schema migrations and realistic test databases.

- Manage cloud database deployments as code with Terraform, standardising how databases are created, configured, upgraded, and retired.

- Drive cloud optimization for the data tier by rightsizing instances, tuning storage and IOPS, managing reserved capacity, and making cost visible to the teams that create it.