Data QE Lead

Technical Requirements

• Solid experience in testing SQL procedures and the ability to understand/write complex SQL queries to perform data validation
• Hands-on experience creating and executing test plans, test cases, and test scripts for Database/ETL Workflows
• Minimum of 2 years of hands-on experience working with AWS services like Glue, S3, RDS, DynamoDB, Lambda, and exposure to Bedrock model integration or experience with other cloud vendors
• AI Tools / Models / Training Data Set exposure is a plus for adoption in testing methodologies
• Hands-on experience analyzing data, comparing with mapping documents, and debugging to identify the root cause
• Familiarity with Docker and Kubernetes; Snowflake and Data Lake
• Hands-on experience Testing and Automation development for Batch Jobs, Data Feeds, API / Web services / Swagger
• Experience with test frameworks like Junit, TestNG; Code Versioning tools like GIT
• Experience with CI/CD with TeamCity/OctopJenkins; and integrate AI-based quality gates and observability into CI/CD pipelines like GitHub Co-pilot
• Envision opportunities and apply AI low code/ no code automation techniques to improvise the test coverage
• Experience with Jira or similar Agile process tools
• Experience with web application and API testing.

Preferences:

• Cloud certification(s) are a plus
• Gen AI hands on around Test preparation, data generation, and automation solutions
• Financial Industry experience
• Ability to thrive in a fast-paced environment where resourcefulness, determination, and strong problem-solving skills are necessary for success
• Positive attitude and ability to take ownership on releases/ features / stories / tasks to deliver with quality and lead scrum teams.

Programming languages: Python and Java