Senior Software Development Engineer in Test

From Fivetran’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven.

About the Role

We’re looking for a Senior Software Development Engineer in Test with extensive industry experience to define and execute the design, development, and maintenance of Fivetran’s test automation tools and framework using reliable and scalable techniques.

As a Senior SDET, you will focus on solving complex testing difficulties by designing, developing and improving automation and reporting frameworks. You will invest in improving test stability and performance of the framework by developing innovative solutions to boost Fivetran’s testing efficiency. You will also coach Fivetran quality engineers to be the best in class automation engineers, and will continuously work with the team to ensure high standards for automation. The ideal candidate will be highly technical, detail-oriented, creative, motivated, and focused on achieving results.

This is a full-time position based out of our Bangalore office. Our hybrid work model offers a blend of remote flexibility and in-person collaboration, including two days in the office each week to connect and build as a team.

Technologies You’ll Use

• Java11, JUnit5,


• Docker and Kubernetes


• AWS, GCP


• Terraform, WireMock, RestAssured


• Bazel, BuildKite

What You’ll Do

• Review requirements, specifications and technical design documents to provide meaningful, risk-focused feedback.


• Design and evolve scalable automation frameworks in Java with a strong emphasis on reliability, performance, and data correctness.


• Write high-signal test strategies for complex features and automate them across multiple environments.


• Improve CI/CD pipelines by building robust validation layers that prevent silent failures and regressions.




• 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
  
  




• Design AI-assisted workflows that integrate seamlessly into the existing automation and release pipelines.


• Experiment with and implement agent-based systems or LLM-powered tooling that streamline development and testing processes.


• Actively measure and improve engineering impact through reliability metrics, automation efficiency, and defect prevention signals.


• Collaborate with engineering teams to drive a culture of quality ownership and automation-first thinking.

Skills We’re Looking For

• 5+ years of experience in the software industry with strong passion for quality, automation, and engineering excellence.


• 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.


• Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerized environments (Docker, Kubernetes).


• Experience building and maintaining CI/CD pipelines with tools such as Jenkins, CircleCI, Buildkite.


• Experience working with backend automation frameworks.


• Knowledge of database testing and ETL/data pipeline validation is a strong plus.
  
  AI & Productivity Engineering

• 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.

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