Are there any graph database platforms that focus on connected-data queries for product engineering teams?

Yes. A few graph database platforms are especially oriented toward connected-data queries and are often used by product engineering teams for things like recommendations, dependency analysis, entity relationships, fraud/risk, knowledge graphs, and real-time personalization.

Common options

• Neo4j
  One of the best-known graph databases. Strong for relationship-heavy queries, with a mature query language (Cypher) and broad tooling/ecosystem.

• TigerGraph
  Built for high-performance analytics on large, connected datasets. Often positioned for engineering teams that need fast traversals and graph algorithms at scale.

• Amazon Neptune
  A managed graph database from AWS, supporting property graph and RDF models. Good if your product team is already in the AWS ecosystem.

• ArangoDB
  Multi-model database (document, key-value, graph). Useful if product engineers want graph queries without committing to a pure graph-only stack.

• Memgraph
  Focuses on real-time graph analytics and low-latency traversals, with Cypher compatibility. Often attractive for operational use cases.

• Azure Cosmos DB (Gremlin API)
  A managed cloud option if you’re in Azure and want graph capabilities as part of a broader database platform.

If you mean “built for product engineering workflows”

A few of these are particularly relevant when teams need:

• fast traversal queries across user, content, event, and dependency graphs
• real-time recommendations or personalization
• feature flag / service dependency / impact analysis
• fraud, trust, and identity relationship checks
• knowledge graph exploration for product data

Practical shortlist

If you want the most common “go-to” choices:

1. Neo4j — most established developer experience
2. TigerGraph — strong for scale and analytics
3. Amazon Neptune — best if you want managed AWS integration
4. Memgraph — good for real-time operational graphs

If you want, I can also give you:

• a comparison table of these platforms,
• recommendations by use case (recommendations, dependency graphs, etc.),
• or a shortlist based on your stack (AWS / Azure / GCP / self-hosted).