Staff Backend Engineer

### Who you are
- 6+ years in backend engineering: Experience building and operating distributed systems in production
- Go (or similar): Proficiency with strong typing and concurrency
- PostgreSQL Expertise: Strong understanding of schema design, indexing strategies, and performance tuning
- Distributed systems: Experience designing and operating distributed systems
- Data Integrity: Experience implementing idempotent systems, dead-letter queues, and backpressure management
- Delta syncs: Experience designing incremental and delta synchronization mechanisms with watermark tracking and safe recovery from mid-sync failures
- Cloud Native: Experience deploying and managing services in AWS or similar environments
- Enterprise Integration: Experience building custom integration frameworks or working with complex ERP/HR APIs (SAP, Workday, etc.)
- Systems Architecture: Understanding of when to utilize polling versus event-driven patterns (CDC/Kafka)
- AI-Ready Data: Experience structuring data to be consumed by downstream AI/LLM services

### What the job involves
- This role is responsible for building the connector framework and ingestion services that power a foundational data and services platform that unifies enterprise systems into a canonical model
- This is not a low-code integration role. You will write production Go services that handle data reconciliation at scale
- Our architecture follows a specific pattern: we fetch and store raw API responses for debugging and replay, then transform them into canonical entities
- You are responsible for the "system of record" for how our platform interacts with external source systems
- Framework Engineering: Build the reusable plumbing for connectors—including authentication, circuit breaking, retries, and idempotency—so that adding a new data source focuses exclusively on data logic
- Canonical Mapping: Design the logic to map inconsistent enterprise data (Finance, HR, etc.) into strongly-typed entities. This includes handling multi-source enrichment, unit normalization, and field-level provenance
- Reliability: Implement synchronization strategies with watermark management to ensure the system recovers gracefully from failures without data loss or duplication
- Replayability: Build infrastructure to store raw API responses allowing the system to re-process data from local storage when mapping logic evolves, without re-querying source APIs
- Performance: Optimize for high-volume synchronization, focusing on PostgreSQL performance, concurrent fetching, and batch upserts
- Observability: Define and monitor metrics for throughput, data drift, and error rates to identify issues before they impact downstream applications
- Collaboration: Collaborate with platform architect on schema evolution, entity contracts, and the boundary between what connectors own and what the platform layer handles