Scaling a SaaS platform to support increasingly complex enterprise environments requires a robust and scalable graph-based data architecture.
Requirements
- Hands-on experience designing, scaling, or operating multi-tenancy databases in production.
- Strong understanding of graph modeling, schema design for nodes/edges, indexing, and query/traversal optimization.
- Proficiency in a modern backend language (Go, Python, Java, Node.js, etc.).
- Solid grounding in distributed systems concepts, partitioning, caching, and performance optimization.
- Experience making architectural decisions in fast-changing, high-scale environments.
- Experience scaling graph-backed SaaS products for enterprise customers with large, complex topologies.
- Familiarity with distributed graph engines, distributed SQL, NoSQL, or horizontally scalable datastores.
Responsibilities
- Architect, evolve, and optimize our multi-tenant graph data model, including node/edge schema design, relationship modeling, partitioning, and traversal strategies.
- Define and implement tenancy models for graph storage—balancing shared-graph vs. tenant-isolated subgraphs, hybrid partitioning, or database-per-tenant graph deployments.
- Develop data architecture for graph sharding, indexing, and high-throughput traversal, ensuring low-latency queries even as topology size and complexity scale.
- Build backend services that interact heavily with datastores, including APIs, data-access layers, ingestion workflows, and graph mutation/query pipelines.
- Optimize query performance through indexing improvements, caching of subgraphs, tuned traversal patterns, and distributed graph execution.
- Own decision-making around multi-tenant graph architecture: tenant boundaries, access control models, resource isolation, and performance fairness.
- Implement tooling for onboarding, migrations, tenant provisioning, graph expansion, and lifecycle management.
Other
- 5+ years of professional software engineering experience in production SaaS environments.
- Collaborate across product, infrastructure, and security teams to ensure the graph layer meets enterprise SLAs, security, and compliance requirements.
- Establish standards around modeling, relationship semantics, data governance, and observability for graph workloads.
- Build visibility into performance and health—metrics, dashboards, anomaly detection, traversal cost profiling, etc.
- Mentor engineers in database fundamentals, scalable SaaS data patterns, and distributed systems thinking.
