Job Summary
Serve as a Test Lead for data intensive and automation focused solutions that use prompt engineering AWS data pipelines Python based frameworks and big data validation to ensure high quality delivery. Coordinate hybrid model testing activities across teams drive defect prevention and support reliable device focused products for customers and society.
Responsibilities
- Lead end to end test planning for data driven and automation heavy projects that rely on prompt engineering AWS data pipelines Python solutions and big data validation to ensure consistently reliable releases.
- Design detailed test strategies that cover functional flows data integrity checks regression suites and non functional aspects so that all critical quality risks are identified and mitigated early.
- Coordinate test case design using systematic techniques and reusable patterns to maximize coverage of data transformation rules automation scenarios and device oriented business workflows.
- Develop robust Python based automation scripts and reusable libraries that validate large data sets orchestrate AWS pipeline jobs and reduce manual testing effort across repeated cycles.
- Configure execute and monitor AWS data pipeline tests by validating data ingestion transformation scheduling and downstream consumption so that data quality and timeliness remain predictable.
- Perform comprehensive big data validation by comparing source to target records boundary conditions and anomaly patterns to detect defects that could affect analytics or device behavior in production.
- Create and maintain test data strategies that include synthetic data design masking rules and data refresh routines to keep test environments stable and representative of real world usage.
- Execute day shift test cycles in a hybrid work model while collaborating closely with distributed engineering data and product teams to keep progress transparent and aligned to milestones.
- Track analyze and report defects with clear steps logs and data samples to support fast triage accurate root cause analysis and timely resolution by development teams.
- Review prompt engineering use cases and outputs from AI enabled components to verify accuracy safety and consistency with expected business behavior and compliance guidelines.
- Collaborate with stakeholders from device engineering and device operations to understand domain scenarios translate them into test cases and validate that solutions support end user needs.
- Prepare regular test progress summaries risk assessments and quality metrics that inform decision making and help the organization release reliable solutions that support customer trust.
- Continuously improve test frameworks automation coverage and data validation practices by analyzing past issues and adopting modern tools that increase efficiency without compromising quality.
Qualifications
- Display strong experience designing and executing tests for AWS data pipelines including data flow validation orchestration checks and monitoring of data quality indicators.
- Demonstrate advanced proficiency in Python automation for building modular test frameworks integration suites and data validation utilities that scale to large volumes.
- Apply practical knowledge of prompt engineering concepts to evaluate prompts expected responses and guardrails for AI driven features from a testing perspective.
- Use hands on big data validation skills across distributed storage and processing platforms to verify completeness correctness and performance of data workloads.
- Bring background in device engineering or device focused domains as a beneficial asset for understanding hardware software interactions and real world usage patterns.
- Show capability to work effectively in a hybrid environment by managing time communication and collaboration tools to support on site and remote team members.
- Exhibit experience of six to ten years in software quality engineering or test roles with increasing responsibility for complex data and automation initiatives.
- Communicate clearly with technical and non technical partners through concise documentation test reports and walkthroughs that enable shared understanding of risks and outcomes.
Certifications Required
Preferred certifications include AWS Certified Data Analytics or AWS Certified Developer and ISTQB Foundation or equivalent testing certification.
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
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