Software Engineer, Data Infrastructure (Staff+)

About the role

ABOUT LIGHTFIELD

Lightfield is reimagining CRM as a world model of a business. By learning from emails, meetings, and customer conversations, we’re building a living understanding of how a company works—so AI can help teams anticipate what comes next, make better decisions, and take action.

More than 5,000 companies have used the product since its November launch. We’ve raised more than $70M from Andreessen Horowitz, Coatue, Maverick Ventures, and Greylock to attack the biggest software category in the world. We previously built Tome, which reached more than 25 million users, and our team brings experience from Llama, Instagram, Google, and Salesforce.

ABOUT THE ROLE

We're building the infrastructure foundation for a fast-growing AI product company serving thousands of customers:

- Our Postgres fleet serves 5B+ queries a month — roughly 2,500 QPS steady state, with sustained spikes past 25,000 QPS — and database workload more than doubled last month.

- Redis sustains ~50,000 commands per second behind a job platform that executes 10M+ background job runs a day across ~170 queues.

- We ingest tens of millions of emails and calendar events a month.

That growth creates scaling pressure across backend systems, infrastructure, and data infrastructure. We're hiring a staff-level engineer who spikes in data infrastructure but is excited to work across backend systems, infrastructure, and product-facing data problems. The work is close to the product, close to customers, and close to production.

As a Software Engineer, Data Infrastructure, you'll build the next generation of our data systems. We got remarkably far on a deliberately simple stack: Postgres as the system of record, a sharded transactional outbox for change events, Redis-buffered sync into Typesense for search, BullMQ for processing, and Postgres-backed customer-facing analytics with per-organization row-level security.

The next phase is evolving that pragmatic foundation into best-practice data architecture: change data capture, event modeling, schema design, query performance, freshness guarantees, and the right boundary between transactional and analytical workloads.

The system of record itself is unusual. Customers define their own objects, attributes, and relationships at runtime, so the core data model is a schema-flexible, graph-shaped store: entity-attribute-value with typed edges, versioned attribute values, and relationship history. That makes schema design, indexing, and query performance genuinely hard problems rather than routine tuning.

The surface area is wider than analytics: customer-facing dashboards, historical and audit data, datasets that power pipeline-generation products, and evaluation data that measures our AI agents. This is data infrastructure work, not a BI or dashboarding role. It's a good fit for someone who likes high-volume data systems, pragmatic architecture decisions, and building foundations that product and engineering teams can actually depend on.