Duco builds cloud-based software that automates data reconciliation and normalization for financial services firms. The platform ingests data from any source, format, or structure and handles the full pipeline - extraction, classification, transformation, reconciliation, validation, exception management, and publication to downstream systems. The core threat model for any team here: financial data at scale is adversarial by nature. Mismatches, format drift, and edge cases are constant. The platform processes billions of records weekly across more than 10,000 users in 30+ countries, which means correctness and uptime aren't aspirational - they're load-bearing.
The technical stack sits at the intersection of cloud computing, artificial intelligence, and data automation. Duco's approach centers on an agentic operations model: software agents handle the repetitive work of reconciling and normalizing data so human operators focus on exceptions and decisions. This isn't a rules engine with a fresh coat of paint - the platform has to reason about heterogeneous financial data across capital markets and other financial services verticals, which means working with formats and schemas that don't play nice.
For security and engineering roles, the stakes are concrete. The platform sits in the critical path of operational workflows at major financial institutions. Data integrity failures propagate fast. The engineering challenge involves building systems that are resilient to malformed inputs, transparent in their reasoning, and auditable end-to-end - all while operating at a volume that makes manual oversight impossible. The team works across the full data lifecycle, from ingestion through exception handling, and the security surface includes every handoff in that chain.
