Software Development Engineer in Test II

Fivetran Fivetran · Data AI · Bangalore, India · Engineering Department

Software Development Engineer in Test II at Fivetran, focusing on defining and executing test automation tools and frameworks. The role involves designing and improving automation frameworks, coaching quality engineers, and leveraging AI/LLM technologies to accelerate development, debugging, test design, and automation activities. Key responsibilities include building AI-assisted tools, workflows, or agents that integrate into engineering pipelines, streamlining workflows using AI, and feeding, tuning, and orchestrating AI agents to solve engineering problems.

What you'd actually do

  1. Design and evolve scalable automation frameworks in Java with a strong emphasis on reliability, performance, and data correctness.
  2. Write high-signal test strategies for complex features and automate them across multiple environments.
  3. Improve CI/CD pipelines by building robust validation layers that prevent silent failures and regressions.
  4. Build internal tools and proof-of-concepts using AI/LLM technologies to: Accelerate debugging and failure triaging Generate high-quality test scenarios Detect flaky patterns or regression risks Improve log analysis and root-cause identification Enhance developer and QA productivity
  5. Design AI-assisted workflows that integrate seamlessly into the existing automation and release pipelines.

Skills

Required

  • Java11
  • JUnit5
  • Docker
  • Kubernetes
  • AWS
  • GCP
  • Terraform
  • WireMock
  • RestAssured
  • Bazel
  • BuildKite
  • OOP principles
  • Java
  • distributed systems risks
  • cloud platforms (AWS, GCP, Azure)
  • containerized environments (Docker, Kubernetes)
  • CI/CD pipelines
  • Jenkins
  • CircleCI
  • Buildkite
  • backend automation frameworks
  • AI/LLM tools
  • AI-assisted tools
  • workflows
  • agents
  • prompt strategies
  • LLM outputs validation
  • safeguards
  • hallucinations
  • unsafe automation
  • AI agents

Nice to have

  • database testing
  • ETL/data pipeline validation

What the JD emphasized

  • extensive industry experience
  • highly technical
  • detail-oriented
  • creative
  • motivated
  • focused on achieving results
  • Deep understanding of OOP principles and strong proficiency in Java.
  • Expertise in designing scalable automation frameworks and identifying negative, edge, and high-risk scenarios.
  • Strong understanding of distributed systems risks such as schema drift, idempotency failures, data inconsistencies, and race conditions.
  • Demonstrated ability to leverage AI/LLM tools to significantly accelerate development, debugging, test design or automation activities.
  • Experience building AI-assisted tools, workflows, or agents that integrate into engineering pipelines.
  • Ability to design prompt strategies, validate LLM outputs rigorously and implement safeguards to prevent hallucinations or unsafe automation.
  • Experience streamlining workflows using AI to improve measurable engineering productivity.
  • Comfortable feeding, tuning and orchestrating AI agents to solve real engineering problems.
  • Strong outcome orientation, measures success through impact, reliability gains and velocity improvements rather than output volume.

Other signals

  • AI/LLM technologies to accelerate debugging and failure triaging
  • Generate high-quality test scenarios
  • Detect flaky patterns or regression risks
  • Improve log analysis and root-cause identification
  • Enhance developer and QA productivity
  • Design AI-assisted workflows
  • Experiment with and implement agent-based systems or LLM-powered tooling