If entity data is wrong, incomplete, or stale, everything downstream fails. Human validation still acts as a safety net in parts of the pipeline. Your mission is to systematically reduce that dependency by increasing correctness, confidence, and trust — without sacrificing speed or scale
You will operate at the intersection of distributed systems, entity resolution, data quality, AI-assisted decision-making, and platform architecture
Own the architecture and evolution of a major area of the entity data platform
Design systems that process large volumes of heterogeneous vendor data with high reliability, freshness, and accuracy
Reduce manual validation overhead through AI-assisted resolution, confidence scoring, provenance, and deterministic matching rules
Establish clear contracts for correctness and traceability — e.g., which source supplied a field, why an entity was matched or created
Balance AI-driven and rules-based approaches where each improves reliability and explainability
Deliver end-to-end integrations across ingestion, matching, entity generation, and delivery
Respond to production incidents in your area; improve observability and reliability through iterative hardening
Set engineering standards, mentor engineers, and partner with product and downstream consumers on quality bars and success metrics
For base compensation, we set standard ranges for all roles based on function and level benchmarked against similar stage growth companies and internal comparables. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location
You may also be offered equity, and a generous benefits program