QA Engineer - Data

The Role:

You will be part of the QA team that is focused on quality on front end embedded client/TV as well as backend cloud services that reports high quality data to customers. The client QA team is responsible to test various embedded clients and firmware for a variety of Vizio TVs. The backend QA team is responsible to validate our award-winning Inscape data set which is used by millions of people every day. We have top-notch software engineers, but with this much data, occasionally, there are errors. That’s where you come in! We’re looking for detail-oriented testers to use our QA team that validates the data and alert us of critical bugs and errors before actual users are affected.

Responsibilities:

• Validates data and ETL pipelines to bring new data into a data warehouse.
• Collaborate with cross-functional teams (Product/Data science/Data engineering) to develop, execute, and automate data testing processes, ensuring that our data assets meet the highest standards of accuracy, completeness, and consistency.
• Identify and research issues reported by internal and external customers.
• Manage defect resolution throughout the lifecycle and ensure issues are resolved prior to production.
• Develop and execute comprehensive data quality tests to identify anomalies, inconsistencies, and data integrity issues for new product development initiatives, product changes, policy changes, database changes.
• Data mining and detailed data analysis on data warehousing systems.
• Create formal test plans to ensure the delivery of data related projects involving applications that use ETL components.
• Provide input and support big data testing initiative.
• Define and track quality assurance metrics such as defects, defect counts, test results, test status, test procedures.
• Verify data accuracy, completeness, and consistency across various data sources and pipelines.
• Create and maintain test data sets for regression testing.
• Provide test support for any issues that require code changes or changes made directly to the ETL pipelines.
• Implement and maintain automated testing framework for data validation.
• Continuously improve and expand test coverage through automation.
• Develop and maintain testing scripts and tools to streamline the testing process
• Collaborate with cross-teams to define data validation rules and criteria.
• Validate data transformations, aggregations, and calculations to ensure accuracy and reliability.
• Maintain comprehensive documentation of data quality issues and resolutions.
• Evaluate and transform documentation into test scripts as needed.
• Work closely with cross-functional teams, including engineers, project managers and other subject matter experts to understand data requirements and validation needs.
• Communicate effectively with stakeholders to report on data quality findings and collaborate on improvements and identify gaps in test coverage.
• Schedule or attend peer reviews of test logic to ensure it has been constructed correctly.
• Communicate with subject matter experts to research source of issues and proposed resolutions, as well as, loading and examining data, business rules, and editorial policy to determine point of failure.
• Perform data testing on new and changed customer output files by reviewing requirements, specifications, and technical design documents and participating in design review meetings.
• Create and support data validation scripts for new and existing ETL pipeline changes.
• Create visualization dashboards to analyze/monitor data for ETL pipeline changes and flag any defects or anomalies in data from regression data testing perspective.
• Design, develop, automation tools to test ETL pipelines.
• Write Python scripts in PySpark for data processing and manipulation

Requirements:

• 2+ years of proven experience in software engineering.
• Bachelor’s degree in computer science, Engineering, or equivalent experience.
• Proven experience as a QA Engineer with focus data/ETL pipeline testing, regression data testing.
• Proven experience with one of BigData technologies such Pyspark, Pandas, Spark, Hadoop, Hive.
• Experience with creation/maintenance of data validation tools and frameworks • Proficient in Python as well as AWS tools.
• Knowledge of data modeling concepts and ETL processes.
• Familiarity with data integration and data warehousing technologies such as Databricks/Snowflake.
• Experience with system integration testing, end-to-end testing, databases, CI/CD pipelines
• Ability to document and troubleshoot errors.
• Strong attention to detail and patience to track down difficult issues.
• Possessing an analytical mind, critical-thinking skills, and problemsolving aptitude.
• Strong organizational skills and ability to meticulously follow detailed steps.
• Experience in creating robust test plans/strategies and test status for Big Data product deliverables.
• Excellent verbal and written communication skills.
• Willing and able to go above and beyond.
• Ability to work collaboratively across different divisions.