Graph Data Engineer

Experience: 5+ Years

2.1 In-Scope Activities

• Knowledge Graph Architecture, Ontology, and Semantic Modeling

He/she will deliver technical capabilities required to design and build a scalable, semantically enriched Knowledge Graph, including:

• Ontology Design & Development
• Creation of formal ontology models aligned with business use cases.
• Use of industry-standard semantic frameworks, including:
  • RDF (Resource Description Framework)
  • OWL (Web Ontology Language)
  • SKOS taxonomy models
• Definition of classes, properties, relationships, taxonomies, constraints, and semantic rules.
• Ontology Deployment & Governance Framework
• Versioned deployment process for ontology schema changes.
• Ontology promotion workflow integrating Our governance gates.
• Semantic modeling validation rules and automated consistency checks.
• Knowledge Graph Data Ingestion Pipelines
• Development of ingestion pipelines for structured/unstructured data.
• Alignment to ontology models and semantic rules.
• Graph model design using Neo4j's property graph model.

• ETL/ELT transformations to map enterprise data to ontology and graph structures.

• CI/CD for Knowledge Graph

• CI/CD for Ontology Change Management

• Git-based versioning of ontology artifacts.
• Automated unit/integration validation of ontology changes.
• Build and release pipelines for ontology deployment across environments.
• Approval workflows for schema evolution and taxonomy updates.
• CI/CD for Data Change Management
• Automated ingestion pipeline deployments.
• Data versioning and rollback capabilities.
• Automated data quality checks, lineage tracking, and deduplication governance.

• GitHub-based source code management for all pipeline components.

• Neo4j AuraDB Integration and Platform Engineering

• Integration of Neo4j AuraDB SaaS

• Configuration of AuraDB instances.
• Connectivity setup with Our's Brownfield Azure environment.
• Role mapping, security integration, and policy alignment.
• Performance Monitoring
• Set up dashboards, alerts, and health checks.
• Baseline performance metrics (throughput, latency, capacity, query bottlenecks).
• SLA monitoring aligned with Neo4j-provided observability tools.

• Collaboration with Data Services Team for long-term operations.

• Integration of Neo4j-Generated ML Features

• Graph Data Science (GDS) Feature Outputs

• Implementation of GDS pipelines (node embeddings, graph metrics, community detection, etc.).
• Delivery of ML-ready feature sets for downstream modeling.
• Integration with Our ML Data Pipelines
• Seamless export of graph features into existing predictive AI workflows.
• Automated refresh of features as KG data changes.

• Integration patterns for Data Science and MLOps pipelines.

• Project Resourcing and Approval Requirements

Candidates must have verifiable:

• Neo4j AuraDB experience
• Knowledge Graph/Ontology engineering background
• Graph Data Science/GDS experience
• Prior delivery of enterprise KG solutions

2.2 Out-of-Scope Activities

• Terraform or Infrastructure-as-Code Provisioning

• Neo4j AuraDB is a fully managed SaaS; infrastructure provisioning is performed by the vendor.

• Terraform or any IaC automation for provisioning Neo4j infrastructure is explicitly out of scope.

• Our will handle connectivity within the Brownfield environment but Terraform modules for Neo4j infrastructure will not be developed.

• General Azure Infrastructure Build

• No Azure resource creation unless explicitly required for pipeline or integration tasks.

• No management of Our enterprise infra services beyond required connectivity.

• Deliverables

3.1 Documentation & Architecture

• Knowledge Graph Reference Architecture
• Ontology Design Document (RDF/OWL Models, Taxonomies)
• Semantic Layer Integration Guide
• Data Mapping Specifications
• CI/CD Pipelines Documentation
• Neo4j Integration & Monitoring Playbook
• ML Feature Integration Patterns Document

3.2 Technical Assets

• Ontologies, semantic models, taxonomies
• Knowledge graph ingestion pipelines
• Ontology CI/CD pipelines
• Data ingestion CI/CD pipelines
• Neo4j configurations, dashboards, alerts (non-infrastructure)

3.3 Operational Deliverables

• Performance baseline and capacity assessments
• Semantic governance model and approval workflow

• Runbooks and support procedures

• Roles & Responsibilities

• Deliver all in-scope architecture, pipelines, ontology development, CI/CD, and integrations.

• Provide qualified resources approved by Our prior to onboarding.

• Provide SMEs for data and business modeling.

• Approve all candidate profiles.
• Provide required access to Azure, GitHub, and enterprise systems.

• Provide XPIQ data infrastructure and warehouse foundation prior to project start.

• Project Governance

• Weekly status calls

• Monthly executive updates
• Deliverable-based acceptance
• Stage-gated progression for ontology and KG development

• Our retains final approval on all architectural decisions

• Acceptance Criteria

• All deliverables completed per requirements

• Ontology models validated and deployed through CI/CD
• Ingestion pipelines operating with QA and lineage
• ML feature generation integrated with DS pipelines
• Performance dashboards operational and baselined