Software Engineer In Test Data Services

Role Summary

Lytx powers real-time safety for thousands of companies worldwide. The Data Services team provides shared capabilities—Schema Registry and data contracts, Data Product Catalog, Data Quality and Observability frameworks, and Data Delivery/Subscriptions—that many products rely on. As a Software Engineer in Test, you will ensure these multi-tenant services are correct, reliable, and measurable before they reach production.

You will own service test infrastructure, API and contract tests, automated data quality signals, and performance/resilience suites. Working closely with software engineers, you’ll read and review service code, design testability into solutions, and enforce CI quality gates so safety insights ship with confidence.

Responsibilities

• Design and implement test infrastructure and harnesses for backend services (Reporting Services, Registry, Catalog, DQ/Observability, Delivery).
• Define and automate data contract tests with versioning and compatibility checks.
• Build automated data quality checks and service observability; verify signals are emitted and actionable.
• Create performance/resilience suites (latency, throughput, error handling) and block releases on regressions.
• Integrate CI quality gates (GitHub Actions/Jenkins) for tests, coverage, and reliability signals.
• Read, review, and collaborate on service code; contribute fixes to improve testability.
• Partner with Enablement and Pipeline teams on shared testing patterns and DQ frameworks.
• Document test strategies, playbooks, and templates; coach teams via office hours and reviews.
• Design effective test cases across unit, integration, system, and contract testing; document test conditions, data setup, and expected outcomes.
  
  

• 5+ years as a software/test engineer validating backend and data services with strong coding skills.
• Proficiency with Python and one typed language (C# preferred); strong SQL.
• API testing (REST/gRPC/event-driven) and consumer-driven contract testing.
• Kafka familiarity and event-driven patterns; basic understanding of lakehouse formats and metadata/catalog.
• CI/CD experience with quality gates; property-based testing and test doubles.
• Solid engineering fundamentals: concurrency, performance profiling, test design, and code reviews.
  
  

• Data testing frameworks (Great Expectations/dbt tests) and observability tooling.
• Experience with Schema Registry (Apicurio) and metadata/catalog systems (OpenMetadata); lineage tooling.
• Performance tools (JMeter/Locust) and custom service perf harnesses; chaos testing.
• Kubernetes and IaC (Terraform/Helm).
  
  

• Defect Escape Rate: Decrease from baseline; incidents caught pre-prod.
• Data Quality Coverage: Increase % services emitting actionable DQ/observability signals with SLAs.
• CI Quality Gates Adoption: Increase % repos enforcing gates (tests, coverage, reliability signals).
• Performance Regressions Blocked: count/ratio; latency and throughput targets met.
• MTTR for Service Incidents: Decrease via better detection and diagnostics.
  
  

• Stages: testing strategy, hands-on service test design/coding, API/contract testing scenario, collaboration.
• Signals: testability, reliability/observability, automation rigor, partnership with engineers.