Richard Webster
Qa Analyst, Distribution Operations at Soundexchange
Based in Washington, United States
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Seniority
Staff
Department
Information Technology
Location
Washington
Industry
Musicians
Company size
269
Contact information
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r•••••••@soundexchange.com
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Background
About Richard Webster
Hi, I'm Richard. I am a Quality Assurance and Data Analysis professional with 4+ years of experience in the Music industry. I support the end-to-end execution of new product development (Inception, POC, MVP, Live). This can be areas in assessing data quality, creating ad hoc reports, and training business users. I'm passionate about researching and developing new products. I enjoy the challenges of working with technical teams and stakeholders to identify process improvement opportunities, system modifications, and data governance strategies. While testing the new CMRRA payment system, I created insightful reports to enhance algorithms. I also optimized test plans and procedures to complete within a tight deadline. This required that I quickly learn new PostgreSQL and AWS systems. It is not always easy making it across the finish line, but it is fulfilling to see hard work pay off. I also enjoy performing data analysis and research. I have worked in large Oracle and PostgreSQL databases to source financial transactions. This meant I had to aggregate disparate information, leveraging both analytic and visualization tools (i.e. Oracle Data Visualizer). Other times I had to clean and process unstructured data sets to complete an analysis. Part of doing analysis is sharing knowledge with others. A team member and I created a Confluence page of common query requests to assist other analysts. In my free time, I enjoy learning new skills and technology. I've recently learned Python to help with testing and data analysis. I want to utilize Python to automate repetitive tasks and reduce errors in test cases. I'm currently learning libraries like Matplotlib, Pandas, and PyTorch for data science purposes. I want to run analyses like regression, decision trees, and k-means by applying what I taught my peers in college to my work. Please reach out if you would like to ask any questions or discuss opportunities.
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