The Role
Design and run automated backend tests validating API contracts, cross-service data integrity, and graph query correctness. Build integration, workflow, and selective end-to-end tests in containerized environments, integrate suites into CI/CD, run basic load checks, and use observability tools to debug distributed services and graph databases.
Summary Generated by Built In
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 queryable 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.
Concrete
examples of what that means in practice:
• Did
the right nodes and relationships get created across multiple services?
• Does a multi-step query return the correct result, not just a plausible one?
• Are data integrity guarantees holding under realistic load and failure conditions?
• Does a multi-step query return the correct result, not just a plausible one?
• Are data integrity guarantees holding under realistic load and failure conditions?
If testing
API contracts and data integrity across a graph sounds interesting, this role
is designed for that.
Scope• 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
LevelingAt 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.
Requirements
API
& Service Quality (Primary)
- 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 smoke
tests against running services
- Prevent
regressions across service boundaries
- Build
tests for critical flows (e.g., ingestion → graph → query → result)
- 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
- Use
logs and dashboards (Grafana + Loki) to investigate failures
- 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)
- Exposure
to load testing tools (any)
Skills Required
- Experience testing backend systems (APIs, microservices)
- Production-quality Python development (not just test scripts)
- Experience with pytest or similar testing frameworks
- Experience designing integration tests across services
- Experience working with CI/CD pipelines
- Comfortable working where requirements are incomplete and tests define expected behavior
- Strong written and spoken English
- 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 (Neo4j, Memgraph, SQL, Cypher)
- Exposure to load testing tools
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
Data Squared develops explainable artificial intelligence for organizations making high-consequence decisions. Its Graph RAG-powered reView platform provides transparent, auditable reasoning across defense and intelligence, energy, supply chain, and financial-services applications. The company positions its mission as making AI work in the real world rather than building another chatbot, with a graph-native analytics and reasoning platform built on microservices architecture for real-world use.
