Staff Backend Engineer, Archer

• 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

Desirable

• 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