QA Automation Lead (Data Engineering)
J
Juniper SquareFinance Technology
Location: India; Workplace: RemoteFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 7–10 years
- Required Skills
- PythonSQLQA AutomationCI/CDLLM
Requirements
- Bachelor's degree in Computer Science, Data Engineering, or equivalent professional experience.
- 7–10 years in Software Quality Assurance with experience leading end-to-end testing efforts.
- Strong proficiency in SQL for data validation, reconciliation, and root cause analysis.
- Strong proficiency in Python including ability to read, understand, and debug pipeline code.
- Solid understanding of data engineering concepts (data pipelines, ETL/ELT workflows, data warehouse architecture, OLAP).
- Proven experience designing and implementing automated test frameworks and CI/CD-integrated testing pipelines.
- Experience using AI-powered tools (e.g., Augment, Cursor, Gemini) for test authoring and debugging.
- Experience managing the full release cycle and ownership of delivery decisions.
- Excellent analytical and problem-solving abilities.
- Strong written and verbal communication skills in English.
Responsibilities
- Partner with Data Engineering and Product leadership to define the data validation and automation strategy for data platform features and new architecture releases.
- 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.
- Leverage AI-powered tools like Cursor or Augment to rapidly prototype, scaffold new test suites, diagnose failures, and generate advanced data validation test scenarios.
- Develop, maintain, and extend scalable data automation frameworks and data quality monitoring suites by leveraging LLMs.
- Establish and enforce data QA best practices, coding standards, and rigorous code review processes for the automation team.
- Champion an automation-first approach to data quality, minimizing reliance on manual data reconciliation.
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