Original listing text, shown exactly as published by the company.
What You'll Do
- Quality Roadmap & Planning: Partner with Data Engineering and Product leadership to define the data validation and automation strategy for data platform features and new architecture releases.
- Backend & Pipeline Testing: Design and execute complex test cases targeting backend data systems, focusing on data integrity, distributed systems logic, data transformation consistency, and asynchronous batch or stream processing.
- AI-Augmented Testing: Leverage AI-powered tools like Cursor or Augment to rapidly prototype, scaffold new test suites, diagnose failures, and generate advanced data validation test scenarios.
- Data Automation Excellence: Develop, maintain, and extend scalable data automation frameworks and data quality monitoring suites by leveraging LLMs.
- Governance & Standards: Establish and enforce data QA best practices, coding standards, and rigorous code review processes for the automation team. Foster a culture of technical excellence and proactive problem-solving.
- Advocate for Automation: Champion an automation-first approach to data quality, minimizing reliance on manual data reconciliation, and partner with data engineering to systematically decrease manual testing effort.
Qualifications
- Education: Bachelor's degree in Computer Science, Data Engineering, or equivalent professional experience.
- Experience: 7–10 years in Software Quality Assurance, including demonstrated ability to lead end-to-end testing efforts across the full software and data lifecycle.
- Database Engineering & SQL: Knowledge of relational databases and strong proficiency in SQL with the ability to write complex queries for data validation, reconciliation, and root cause analysis.
- Data Infrastructure & Concepts: Solid understanding of data engineering concepts including data pipelines, ETL/ELT workflows, data warehouse architecture, and OLAP technologies (e.g., Redshift, Snowflake, BigQuery, or equivalent).
- Programming Skills: Strong proficiency in Python including the ability to read, understand, and debug data pipeline code.
- QA Automation Frameworks: Proven experience in QA automation, including designing and implementing automated test frameworks, test suites, and CI/CD-integrated testing pipelines.
- AI-Augmented Development: Proactive in using AI-powered tools (e.g., Augment, Cursor, Gemini) to accelerate test authoring, assist in debugging automation scripts, and optimize data documentation workflows.
- Release Ownership: Experience managing the full release cycle for data features, from scoping testing requirements to final "Go/No-Go" delivery decisions.
- Soft Skills: Excellent analytical and problem-solving abilities, exceptional attention to detail, and the ability to work independently in fast-paced Agile development teams with minimal supervision.
- Communication: Strong written and verbal communication skills in English
.
AI & LLM Testing Experience
- Proactively leverage AI tools (e.g., Cursor, Gemini) to accelerate test authoring, debugging, and maintenance of data automation frameworks.
