Graph and Data Engineer

Zero Hash

. Build a canonical entity and knowledge graph across Intelligence Core connectors to resolve identities .

Posted 10/13/2026full-timeRemote • United States, United Kingdom, NetherlandsMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in entity resolution, knowledge graph modeling, and privacy-preserving data practices. Proficient in Python and applied machine learning for scalable identity systems within regulatory frameworks.

Highest-signal resume keywords

Entity ResolutionKnowledge Graph ModelingPython ProgrammingApplied Machine LearningRegulatory Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Entity ResolutionKnowledge Graph ModelingPython ProgrammingGraph Data EngineeringMachine LearningSchema DesignAPI DesignBitemporal ModelingAnomaly DetectionData Permissions

Soft Skills

Collaboration with Finance and ComplianceDiscipline in BenchmarkingComfort with Uncertainty

Tools & Technologies

SlackJiraIronclad

Industry Keywords

Privacy by DesignIdentity SystemsMSB/VASP EnvironmentData PartitioningOperational Throughput

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build a canonical entity and knowledge graph across Intelligence Core connectors to resolve identities
Lead the classify-at-source pilot with Finance and Compliance to establish unified tag models and metrics
Establish shadow-AI and agent-identity registers defining ownership, access controls, and data permissions
Build entity resolution pipelines across Slack, Jira, and Ironclad using privacy-preserving linkage and canonical IDs
Develop graph-based correlation capabilities and establish baseline metrics for operational throughput and SAR false positives
Ground operational agents in the shared graph for unified event tracking, reasoning, and real-time authorization
Define graph architecture, schemas, temporal permissions, and provenance tracking
Maintain data accuracy with bitemporal modeling, automated drift detection, and historical replay
Model delegation chains and agent credentials anchored to human principals
Deploy ML and graph-native methods to analyze multi-agent behavior, 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 standards

Requirements

What you’ll need

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
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
Experience designing for an identity layer in an MSB/VASP environment
Comfort building a foundational layer whose consumers don't exist yet

Benefits

Comp & perks

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)