Context Engine - GCP Data and graph engineer

Context Engine - GCP Data and graph engineer

Employment Type: Contract-to-Hire (possible 3-month CTH) Location: Toronto, Ontario Work Mode : Hybrid

Job Summary Our client is looking for a highly skilled Data & Graph Engineer to build and fine-tune graph-driven data platforms, search infrastructure, and event-based data architectures. You'll bring strong graph modeling and query expertise, along with a solid grasp of search datastores, knowledge graphs, and cache/index invalidation. Together these will power fast, scalable data retrieval and analytics. You'll work alongside product, architecture, platform, and engineering teams to create intelligent data relationships, robust search capabilities, and real-time data flow across enterprise systems.

Key Responsibilities

• Model complex business domains and entity relationships as scalable graph structures.
• Write and tune graph queries using Cypher (Neo4j), and GraphQL where it applies.
• Build and maintain knowledge graphs and relationship-focused data platforms.
• Design and optimize search using Elasticsearch/OpenSearch, vector databases, and hybrid search approaches.
• Build pipelines that keep transactional, graph, and search systems in sync.
• Implement event-driven architectures for near real-time data updates.
• Design cache and index refresh logic, with event-driven invalidation, to keep distributed systems consistent.
• Work with application teams to shape search relevance, ranking, and retrieval strategies.
• Put monitoring, observability, and data quality checks in place across graph and search platforms.
• Improve query performance, indexing strategies, and data access patterns.
• Set up governance, lineage, and metadata management practices for the graph ecosystem.

Required Qualifications

• Strong expertise in graph data modeling and graph query languages, especially Cypher (Neo4j).
• Hands-on experience with search datastores such as Elasticsearch or OpenSearch.
• Experience building and managing knowledge graphs.
• A solid understanding of cache and index invalidation strategies for low-latency data retrieval.
• Experience with event-driven architectures and real-time data synchronization.
• The ability to collaborate effectively across product, architecture, platform, and engineering teams.

Nice to Have

• Experience designing knowledge graphs and ontologies.
• Background in recommendation engines or relationship analytics.
• Exposure to GenAI, RAG, and GraphRAG architectures.
• Experience integrating vector stores with graph databases.
• Familiarity with Kubernetes and cloud-native deployments.
• Infrastructure as Code experience (Terraform, CloudFormation).