Software Engineer, Data Engineering

Position Summary

Judi Health is building the technology infrastructure our nation needs to deliver the healthcare we deserve - a domain where correctness is non-negotiable and latency has real-world consequences. The Architecture organization is responsible for the technical foundation the rest of the company builds on: the standards, patterns, and infrastructure primitives that engineering teams depend on to move fast and build correctly.

The Performance team sits within Architecture's Core Platform function, owning the platform's ability to understand, measure, and enforce how systems behave under load.

As a Staff Engineer in Architecture, your influence extends beyond what you ship directly. The standards you set, the patterns you establish, and the decisions you document become the reference point for how system performance is measured and managed across the organization. You are building for the platform and for the teams that build on it.

Claims adjudication, prior authorization, and auxiliary data processing drive query patterns at a volume and complexity where the interaction between schema design, cluster configuration, and connection behavior determines whether the platform holds under load with acceptable performance. You will be directly responsible for identifying where the ceiling is, prioritizing changes that move it with minimal disruption, and driving deeper re‑architecture where the problem demands it.

One of your first major initiatives will be a comprehensive diagnostic of the platform's database architecture, focused on the systems powering claims adjudication and claims processing - the most performance-sensitive workflows on the platform. This is a deeply embedded engagement; you will work directly within these domains to understand the query patterns, connection behavior, and cluster configuration shaping how the system performs under load.

From there, you will establish the benchmarking frameworks and performance baselines that give the engineering organization a reproducible way to evaluate parameter changes, schema modifications, and database upgrades before they reach production.

In this role, you'll own the observability infrastructure that powers our engineering organization. You will:

• Apply deep PostgreSQL expertise to diagnose production performance problems - interpreting query plans, analyzing usage patterns, and evaluating how cluster configuration and architectural decisions shape system behavior at scale – then own the path from root cause to production remediation
• Evaluate and implement cluster topology changes and drive the architectural transformations – including the migration from synchronous to asynchronous I/O patterns – that take the platform's throughput to the next order of magnitude
• Build the benchmarking and regression-detection infrastructure that gives engineering teams a database performance signal in CI, before changes reach production
• Define and own performance contracts for the database layer: latency targets, throughput expectations, and the SLA boundaries that upstream services build against
• Lead query and schema reviews for high-risk changes; own incident response for performance-related degradations and drive post-incident findings into durable engineering improvements
• Mentor and develop senior engineers on PostgreSQL internals, query analysis, execution plan interpretation, and the performance implications of schema decisions at scale
• Participate in a 24/7 on-call rotation to provide continuous operational support and rapid incident response

• 10+ years of production engineering experience, with deep specialization in PostgreSQL performance and transactional systems at scale
• Deep fluency in PostgreSQL internals: MVCC, autovacuum behavior, query planner statistics, locking semantics, and the interaction between transaction patterns and long-running queries
• Demonstrated ability to diagnose and remediate production database performance problems in regulated or correctness-critical environments – WAL write amplification, buffer pool cache pressure, reader/writer usage constraints from replica lag, and…