Graph and Data Engineer
Overview:
In this role you will design and operate a canonical identity and knowledge graph across enterprise connectors to support automated system-level identity, authority, and permissions. You will build and govern an auditable, privacy-conscious entity layer that underpins security, compliance, and external interoperability. You will work closely with Finance and Compliance to pilot unified tag models, and you will apply ML and graph methods to monitor multi-agent behavior and detect anomalies. This position offers scope to shape core data governance and real-time authorization at scale within a regulated crypto infrastructure context. Compensation / Benefits 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 MembershipWFH Yearly Stipend
Responsibilities:
- Lead the classify-at-source pilot with Finance and Compliance to establish unified tag models and metrics
- 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 IDsDevelop graph-based correlation capabilities and establish verifiable baseline metrics for 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 for full auditability and agent compatibility
- Maintain data accuracy with bitemporal modeling, drift detection, and historical replay 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 regulatory compliance, jurisdictional data partitioning, and privacy-by-design across the identity layer
Key requirements:
- 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, including embedding- and LLM-based matching with benchmarking on synthetic/de-identified data
- Schema and API design for heterogeneous consumers (human and machine)
- Comfort working with Finance and Compliance on live data pilots
- Security and privacy by design mindset in regulated environments
- Ability to build foundational layers for future consumer usecollaborativeinitiativeproblem-solving
- Pythongraph/data engineeringML for resolution at scale