Senior QA Data Layer Manager

3 weeks ago

Makati, Philippines Dempsey Resource Management Inc. Full time

Responsibilities Lead and manage a team of QA professionals, including manual testers and automation engineers, with a specific focus on API and ETL-related testing. Collaborate with cross-functional teams—product management, development, Customer support, and customer success—to ensure quality across the software development lifecycle. Oversee API and ETL testing efforts, ensuring thorough validation of data pipelines, transformations, and API functionalities. Identify process improvement opportunities and implement strategies to enhance product quality, testing efficiency, and team productivity. Provide hands-on expertise and deep understanding of API and ETL processes, guiding the team to ensure complete and accurate testing coverage. Collaborate with the Automation and Tooling team to enhance automation frameworks specifically for API and ETL testing, in addition to managing the execution of both automated and manual tests. Qualifications Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience). Minimum of 5 years of management experience, overseeing a QA team with at least 6 direct reports. Strong experience in API testing and ETL processes, with the ability to guide team members on testing strategies for data integration, pipelines, and transformations. Hands-on experience with automation frameworks specifically for API and ETL testing. Proven track record of working collaboratively with cross-functional teams to identify, troubleshoot, and resolve issues related to API and ETL workflows. Solid understanding of SaaS-based applications and their unique testing requirements, with a focus on data validation and integrity. Extensive knowledge of different data stores, databases, and big data technologies, including: Cloud data warehousing solutions such as Snowflake, Amazon Redshift,Google BigQuery, or Azure SQL. Traditional relational databases like PostgreSQL, MySQL, Oracle, or SQL Server. NoSQL databases including MongoDB, Cassandra, or Elasticsearch. Big data technologies and data lakes such as Hadoop, S3, Azure Data Lake, or HDFS for large-scale data processing and storage. #J-18808-Ljbffr

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