Graph and Data Engineer

This role builds a canonical entity and knowledge graph across Intelligence Core connectors to resolve identities (people, agents, systems, data entities). The entity layer supports data classification at the source to unify transaction reporting.

Core Objective: Automate system-level identity, authority and permissions.

Strategic Importance: Underpins security, core authority models, registers, and kill switches internally, while providing the foundation for agentic commerce credentials externally.

• Lead the classify-at-source pilot with Finance and Compliance to establish unified tag models and metrics without re-derivation.
• Establish shadow-AI and agent-identity registers to define ownership, access controls, and data permissions.
• Build entity resolution pipelines across internal systems (Slack, Jira, Ironclad) using privacy-preserving linkage and canonical IDs.
• Develop graph-based correlation skills (e.g., Jira gap-finder) and establish verifiable baseline metrics for operational throughput and SAR false positives.
• Ground operational agents in the shared graph to deliver unified event tracking, reasoning, and real-time authorization.
• Define graph architecture, governing schemas, temporal permissions, and provenance tracking to ensure full auditability and agent compatibility.
• Maintain data accuracy using bitemporal modeling, automated drift detection, and historical replay capabilities for compliance.
• Model delegation chains and agent credentials anchored to human principals for secure external interoperability.
• Deploy ML and graph-native methods to analyze multi-agent behaviors, detect anomalies, and identify shared lineage or prompts.
• Evaluate and integrate emerging graph and identity resolution research.
• Enforce strict regulatory compliance, jurisdictional data partitioning, and privacy-by-design standards across the identity layer.

• Direct experience with entity resolution, identity systems or knowledge-graph modelling.
• Strong Python plus graph or relational data engineering in production.
• Applied ML for resolution at scale, potentially including embedding- and LLM-based matching, self-supervised and active-learning loops, with the discipline to benchmark before trusting, on synthetic or de-identified sets, since raw PII never enters an evaluation.
• Schema and API design for heterogeneous consumers, human and machine.
• Comfort working directly with Finance and Compliance colleagues on a live data pilot from the first weeks.
• Security and privacy by design as a working habit: an identity layer in an MSB/VASP environment is regulated surface, and you design accordingly.
• Comfort building a foundational layer whose consumers don't exist yet.

• Healthcare Insurance: zerohash covers roughly 100% of employee premiums as well as a portion of spouse/children (U.S. only)
• Vision & Dental Insurance (U.S. only)
• Chance to earn equity
• Maternity & Paternity leave (after 6 months)
• WeWork All Access Membership
• WFH Yearly Stipend
• L&D Yearly Stipend (after 6 months)

zerohash is the leading crypto and stablecoin infrastructure platform and the operating system for digital money, powering the next generation of financial services. Founded in 2017, zerohash enables banks, brokerages, fintechs, and payment companies to offer crypto trading, stablecoin payments, and tokenized assets without having to build or manage complex blockchain infrastructure on their own.

The zerohash platform supports three core pillars:
Trade, Transact, and Tokenize. Through a single integration, customers can launch regulated crypto buy, sell, and custody services; enable 24/7 stablecoin-based funding, payouts, and settlement; and issue and manage tokenized assets with built-in compliance,…