Senior Software Development Engineer in Test - Database Automation

About the Role

Fivetran is building data pipelines to power the modern data stack for thousands of companies.

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.

In addition, this role emphasizes leveraging AI driven solutions to accelerate testing, enhance developer productivity, and improve operational insights, making Fivetran’s quality practices smarter, faster, and more scalable.

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.
• Collaborate with engineering and product leaders to align testing priorities and business objectives.

Skills We’re Looking For

• 6+ 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, Buildkite.
• Experience working with backend automation frameworks.
• Testing data pipeline or ELT platforms, with hands-on exposure to Change Data Capture (CDC) mechanisms, connector-based data ingestion, and validating data accuracy and consistency across source and destination systems at scale.
• Deep understanding of database internals
• 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.

Bonus Skills

• Experience testing against cloud data warehouses (Snowflake, BigQuery, Redshift) or CDC source databases (Oracle, SQL Server, PostgreSQL, MySQL)
• Familiarity with log-based CDC mechanisms (binlog, WAL, ) and handling schema drift or evolution scenarios.
• Exposure to distributed systems testing, large-scale data reconciliation, and containerized environments (Docker, Kubernetes) on cloud platforms (AWS/GCP/Azure).
• Familiarity with data extraction using API-based polling, batch/file ingestion, and query-based incremental syncs.