Intelliswift - An LTTS Company
Quality Assurance Developer - Dev QA (Cupertino)
Intelliswift - An LTTS Company, Cupertino, California, United States, 95014
Key Responsibilities
Data Validation & Comparison
Compare
Excel output vs JSON output
to ensure correctness, completeness, and structural integrity. Validate schema, key-value pairs, formatting, and business rules. Normalize and flatten JSON to align with Excel tabular formats. Write and maintain Python scripts (Pandas/JSON libraries) for automated data comparison. Quality Assurance Create detailed test plans, test scenarios, and test cases for data validation workflows. Perform functional testing on services, APIs, and data pipelines that generate outputs. Identify defects, analyze root causes, and work closely with developers to resolve issues. Validate regression outputs to prevent data drift across releases. Documentation & Reporting Document data comparison rules, testing procedures, and validation logic. Provide clear defect reports with reproducible steps and detailed examples. Create and maintain QA dashboards, logs, and reports as required. Team Collaboration Work cross-functionally with Development, Product Engineering teams. Drive QA standards, best practices, and improvements to validation processes.
Required Skills & Qualifications Technical Skills Strong proficiency in
Python
(Pandas, JSON parsing, data transformation). Advanced
Excel
skills (VLOOKUP/XLOOKUP, pivot tables, conditional formatting). Experience with
JSON , nested data structures, and schema validation. Familiarity with
API testing
using tools like Postman or similar. Experience with data diff tools (VS Code diff, Beyond Compare, WinMerge). Solid understanding of QA methodologies, functional testing, and defect lifecycle. Analytical Skills Ability to analyze complex datasets and identify inconsistencies. Strong problem-solving skills and ability to debug logical errors. Ability to interpret business rules and apply them to data validation.
Bonus Skills Experience with SQL (joins, filters, data validation). Knowledge of automation frameworks (PyTest, Robot Framework). Experience with Jupyter Notebooks for data visualization. CI/CD pipeline familiarity for automated test execution. Understanding of cloud-based storage (AWS S3, Azure Blob).
Education & Experience Bachelors degree in Computer Science, Information Systems, Engineering, or related field. 37 years of experience in QA, Data QA, Data Validation, or Data Engineering QA roles. Experience validating outputs from APIs, ETL pipelines, or reporting systems is highly desirable.
Excel output vs JSON output
to ensure correctness, completeness, and structural integrity. Validate schema, key-value pairs, formatting, and business rules. Normalize and flatten JSON to align with Excel tabular formats. Write and maintain Python scripts (Pandas/JSON libraries) for automated data comparison. Quality Assurance Create detailed test plans, test scenarios, and test cases for data validation workflows. Perform functional testing on services, APIs, and data pipelines that generate outputs. Identify defects, analyze root causes, and work closely with developers to resolve issues. Validate regression outputs to prevent data drift across releases. Documentation & Reporting Document data comparison rules, testing procedures, and validation logic. Provide clear defect reports with reproducible steps and detailed examples. Create and maintain QA dashboards, logs, and reports as required. Team Collaboration Work cross-functionally with Development, Product Engineering teams. Drive QA standards, best practices, and improvements to validation processes.
Required Skills & Qualifications Technical Skills Strong proficiency in
Python
(Pandas, JSON parsing, data transformation). Advanced
Excel
skills (VLOOKUP/XLOOKUP, pivot tables, conditional formatting). Experience with
JSON , nested data structures, and schema validation. Familiarity with
API testing
using tools like Postman or similar. Experience with data diff tools (VS Code diff, Beyond Compare, WinMerge). Solid understanding of QA methodologies, functional testing, and defect lifecycle. Analytical Skills Ability to analyze complex datasets and identify inconsistencies. Strong problem-solving skills and ability to debug logical errors. Ability to interpret business rules and apply them to data validation.
Bonus Skills Experience with SQL (joins, filters, data validation). Knowledge of automation frameworks (PyTest, Robot Framework). Experience with Jupyter Notebooks for data visualization. CI/CD pipeline familiarity for automated test execution. Understanding of cloud-based storage (AWS S3, Azure Blob).
Education & Experience Bachelors degree in Computer Science, Information Systems, Engineering, or related field. 37 years of experience in QA, Data QA, Data Validation, or Data Engineering QA roles. Experience validating outputs from APIs, ETL pipelines, or reporting systems is highly desirable.