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Test Lead
6-13 years
₹ 10 - 17.6L/yr (AmbitionBox estimate)
This is an estimate of the average salary range for this
position. It has not been reviewed by the company, and
the actual salary may differ.
Bangalore / Bengaluru
1 vacancy
Test Lead
Infinite Computer Solutions
posted 9d ago
Job Role Insights
Flexible timing
Key skills for the job
Job Description
A QA Lead with BigQuery focuses on leading quality assurance activities for data-driven projects, especially those that leverage Google BigQuery as a data warehouse or analytics platform. The QA Lead is responsible for ensuring the accuracy, performance, and reliability of the data stored and queried in BigQuery, along with ensuring the overall quality of data pipelines, analytics, and business intelligence (BI) outputs.
Roles and Responsibilities of a QA Lead with BigQuery:
Roles and Responsibilities of a QA Lead with BigQuery:
- Quality Assurance Strategy and Planning:
- Develop and implement a comprehensive QA strategy for projects involving BigQuery, focusing on ensuring data accuracy, integrity, and performance.
- Define test strategies, including test automation and manual testing, for BigQuery data pipelines, SQL queries, and reports.
- Collaborate with stakeholders to understand the project scope and requirements for data quality, performance, and analytics.
- Test Planning and Execution:
- Design, execute, and monitor test cases for data validation, data transformation, data loading, and querying in BigQuery.
- Perform regression testing, integration testing, for BigQuery queries and data pipelines.
- Conduct tests to validate data accuracy, ensuring that queries return correct and expected results.
- Monitor and evaluate the performance of BigQuery queries, ensuring they run efficiently even with large datasets.
- Automation of Testing Processes:
- Develop and maintain automated test scripts for testing data quality, transformation logic, and reporting within the BigQuery environment.
- Implement automated testing tools and frameworks, such as dbt (data build tool), to help with data testing and validation.
- Ensure continuous integration (CI) pipelines are in place for automated testing and feedback during the development lifecycle.
- BigQuery Query Optimization and Validation:
- Review and validate SQL queries used in BigQuery for performance optimization and ensuring they meet the business requirements.
- Monitor query execution times, identify bottlenecks, and work with developers to optimize queries.
- Collaboration with Data Engineers and Developers:
- Work closely with data engineers to validate data ingestion pipelines and data transformations within BigQuery.
- Collaborate with BI teams to ensure the quality of data presented in dashboards and reports.
- Ensure the data being loaded into BigQuery is accurate, complete, and consistent, based on defined requirements and data sources.
- Data Integrity and Accuracy Monitoring:
- Establish mechanisms for ongoing monitoring of data quality in BigQuery, including validating input and output data.
- Track data errors, discrepancies, and anomalies, and ensure corrective actions are taken.
- Conduct regular audits of the data quality and ensure that best practices for data governance are followed.
- Reporting and Documentation:
- Prepare and maintain detailed documentation for QA processes, test cases, and test results.
- Provide regular reports to stakeholders, such as developers, product managers, and leadership, on the status of testing, data quality, and performance metrics.
- Communicate issues, risks, and bottlenecks related to data quality and performance effectively.
- Performance Monitoring and Reporting:
- Monitor and address issues related to query performance, data load times, and system resources.
- Optimize cloud resource utilization to minimize costs while maintaining performance.
- Issue Resolution and Troubleshooting:
- Investigate data issues and collaborate with the data engineering and development teams to resolve data discrepancies, pipeline failures, or performance issues.
- Utilize tools such as Google Cloud Monitoring, logging, and debugging features to identify and resolve problems in BigQuery and associated data systems.
- Technical Expertise:
- Strong knowledge of Google BigQuery, SQL, and data warehousing principles.
- Experience with data testing tools and frameworks (e. g. , dbt, Apache Airflow).
- Familiarity with ETL processes, data pipelines, and cloud infrastructure.
- Expertise in query optimization and performance tuning in BigQuery.
- Proficiency in scripting languages for test automation (e. g. , Python, Shell scripting).
- Google BigQuery
- SQL (for writing and optimizing queries)
- dbt (Data Build Tool)
- Google Cloud Console and Google Cloud SDK
- Data pipeline orchestration tools (e. g. , Apache Airflow, Prefect)
- CI/CD tools for automated testing (e. g. , Jenkins, GitLab CI)
- Performance monitoring tools (e. g. , Google Cloud Monitoring)
Employment Type: Full Time, Permanent
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What people at Infinite Computer Solutions are saying
Rating based on 19 Test Lead
reviews
Anonymous · Engineering - Software & QA
in Bangalore / Bengaluru
Likes
Its a second home...
- Skill development - Good
+3 more
Dislikes
Projects are not ling term
Test Lead salary at Infinite Computer Solutions
reported by
191
employees
with 5-13
years exp.
₹8
L/yr - ₹22
L/yr
At
par with the average Test Lead Salary in India
View more details
What Infinite Computer Solutions employees are saying about work life
based on 1.4k employees
Flexible timing
Monday to Friday
No travel
Day Shift
Infinite Computer Solutions Benefits
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