Software Development Engineer in Test (Backend)

We are looking for a Backend SDET for the Anti-Scam BU Common Service Team. Your primary focus will be developing automated tests and internal testing tools using Python to continuously measure the quality of backend services and data pipelines, shifting away from manual regression.

You will work directly with PMs, RDs and SREs, taking ownership of API and data pipeline test design and implementation, test framework maintenance and extension, and ensuring the stability of automated tests in CI/CD.

Our test frameworks, tools, and CI scripts are exclusively written in Python. We expect your Python proficiency to be at a level where you can write maintainable test code that others are willing to take over.

We expect you to make independent decisions within your scope of responsibility and proactively communicate any identified risks. Cross-team architectural decisions and priority trade-offs will be discussed directly with the QA Lead.

Key Responsibilities

• API Testing: Design and implement automated tests for API behaviors, covering functional, regression, and contract levels. Since multiple product lines depend on the Common Service, contract verification for APIs and event schemas is a key focus of this role.
• Data Flow & Pipeline Validation: Design and implement automated validation for queues, events, scheduled jobs, and data pipelines. This covers data correctness, completeness, duplication/loss prevention, processing latency, and idempotency (result consistency upon rerun).
• Test Environment & CI Pipeline: Maintain and extend automated tests within the existing pipeline architecture, and propose improvements for execution time, stability, and coverage. Maintain the reliability of existing tests, including identifying and resolving flaky tests. Deploy test environments following standard procedures and troubleshoot environment anomalies independently.
• Test Framework & Internal Tools: Maintain and expand the test framework based on existing project structures and conventions, including shared fixtures, type hinting, and test data management. Additionally, develop internal tools to automate repetitive manual verification tasks for RDs or PMs.
• Performance Testing: Participate in the implementation of performance and load testing (Locust, JMeter), including script writing, scenario configuration, and result interpretation.
• AI Integration in Workflows: Leverage coding agents and LLMs for test writing, test data generation, log analysis, and failure attribution. Document reusable prompts, specifications, and guidelines to ensure stable output quality from these tools across the team.

Minimum Qualifications

• 3+ years of experience in software development or test automation.
• Proficient in Python development, including the pytest ecosystem, virtual environments, package management, and type hinting; able to write well-structured, maintainable code.
• Experience working with asynchronous processing systems (queues, events, background scheduling) or data pipelines, with a solid understanding of the testing challenges introduced by eventual consistency.
• Solid understanding of HTTP/API, databases, and the operational principles of AWS cloud services (e.g., DynamoDB, Lambda, Systems Manager).
• Hands-on experience with CI/CD practices, such as GitHub Actions.
• Strong grasp of QA fundamentals: understanding the appropriate use cases for different testing levels, coverage strategies, and knowing what is not worth testing.
• Proven integration of AI tools into daily workflows, with the ability to clearly articulate the scenarios where they are effective or ineffective.
• Proactively communicates technical risks; conscious of technical debt, willing to track it, and actively participates in resolving it.
• Able to find answers independently when information is incomplete and adapt approaches based on past mistakes.

Preferred Qualifications

• Experience with contract testing, test environment provisioning, and test data management.
• Experience using observability tools (Datadog, Grafana).
• Basic operational knowledge of Docker, Kubernetes, or Terraform.
• Proven track record of developing internal tools that were actively adopted by other teams.
• Experience in quality validation of AI features (e.g., evaluation methods for non-deterministic outputs).
• Ability to conduct technical discussions in both English and Mandarin.