Principal Backend Engineer

Seattle, United States | Posted on 04/28/2026

From disconnected data to confident, explainable decisions

data² delivers trustworthy, explainable AI (eXAI) that automates critical workflows and turns complexity into clarity. Our patented, hallucination-resistant eXAI reView platform harmonizes data across your ecosystem delivering real-time actionable insights.

reView is a microservices backend over a graph data layer. Correctness in our system depends not just on API behavior, but on whether data is correctly structured, linked, and query‑able across services. In a regulated‑industry product, the difference between a result that runs and a result that is right is the entire value of the platform.

• Backend and data‑focused testing (not UI‑heavy)
• Integration and workflow correctness over broad end‑to‑end coverage
• Deeper performance and full‑system validation evolve over time
• Embedded with the platform team, pairing closely with backend engineers
• Local and test environments are containerized (Docker‑based), with shared staging for integration validation

Leveling:
At the mid level, you will execute and extend an evolving test strategy. At the senior level, you will shape that strategy and influence how the platform is built for testability.

• Design and maintain automated tests for FastAPI services
• Validate request/response schemas, error handling, and auth flows
• Write tests across layers: unit tests (targeted handler‑level validation), integration tests (service‑level using test environments), and API‑level smoketests against running services
• Prevent regressions across service boundaries

• Validate behavior under realistic conditions (retries, partial failures, async flows)
• Ensure consistency of data across services

• Verify correctness of node and relationship creation in Neo4j / Memgraph
• Validate key queries and multi‑hop traversals against expected outputs
• Detect issues such as missing or incorrect relationships, duplicate entities, broken identity assumptions, and incorrect mappings during ingestion
• Define and evolve the approach to graph test fixtures (data seeding, isolation, repeatability)

• Implement a small number of high‑value end‑to‑end or API‑level tests
• Focus on critical workflows rather than broad UI coverage
• Use pragmatic approaches (e.g., pytest‑driven flows, containerized environments)

• Integrate test suites into CI pipelines
• Define and enforce quality gates for merges and releases (coverage thresholds, integration test pass rates, graph‑integrity checks)
• Maintain test reliability and reduce flakiness

• Run basic load and stress tests using standard tooling – e.g., recurring load tests to catch regressions in core ingestion and query paths
• Identify obvious bottlenecks in APIs and graph queries
• Collaborate with engineers on scaling behavior in Kubernetes

• Trace issues across services and data layers
• Help reproduce production issues locally and in test environments

• Experience testing backend systems (APIs, microservices)
• Comfortable reading and writing production‑quality Python (not just test scripts)
• Experience with pytest or similar frameworks
• Experience designing integration tests across services
• Experience working with CI/CD pipelines
• Comfortable working in systems where requirements are incomplete and tests help define expected behavior
• Strong written and spoken English skills for cross‑border collaboration

• Experience with FastAPI or similar Python frameworks
• Experience working in Kubernetes or distributed systems
• Experience testing data pipelines or ETL workflows
• Familiarity with graph or query‑based systems (e.g., Neo4j, Memgraph, SQL, Cypher)