Software Engineer, Data Infrastructure (Staff)
$180,000–$300,000 year
HybridSan Francisco, California, United States or Cambridge, Massachusetts, United States
Job Summary
Scale the analytics engine behind customer-facing dashboards, tackling query performance under row-level security, workload isolation, read architecture, and observability as data volume grows. Design ingestion paths, event models, schemas, and query patterns that move data from transactional writes into search, dashboards, and history with clear guarantees around freshness, correctness, and failure recovery. Evolve the schema-flexible, graph-shaped data model so customer-defined objects, attributes, and relationships remain fast to query as their size and complexity grow. Build foundations for historical reporting, auditability, usage metering, and AI evaluation data systems where errors have direct customer or financial consequences. Set technical direction, abstractions, and ownership boundaries for data systems as the company scales. This role focuses on evolving Lightfield's pragmatic Postgres and Redis foundation into best-practice data architecture for a fast-growing AI-native CRM.
Required Qualifications
- Strong software engineering fundamentals
- Experience owning production data systems where query plans, replication lag, backfills, data freshness, schema evolution, or data correctness had real user-facing consequences
- Comfort debugging across multiple layers of the stack
- Good judgment about when to make a tactical fix and when to invest in a more durable platform or architecture change
- Product orientation: you care about how data infrastructure decisions affect customers, users, and engineering velocity
- Clear communication, strong ownership, and a bias toward practical tradeoffs
- Postgres at scale, and the boundary between OLTP and OLAP systems
- APIs, queues, workflow systems, and distributed systems
- Observability, incident response, service ownership, and production debugging
- Data for ML/AI systems: enrichment pipelines, eval harnesses, or data-quality tooling
- Experience in a high-growth product environment
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