Analytics Engineer
CurrentBy diving deep into data across QA, UAT, and Production environments, I was able to pinpoint failure patterns that were causing inefficiencies in credit card payments, ultimately leading to a 25% reduction in issues. Using a combination of A/B testing, SQL, and Excel tools like VLOOKUP and PivotTables, I identified the root causes, which directly improved the payment flow. But my work didn’t stop there. By seamlessly integrating Salesforce data into Tableau dashboards, I built an analytics routine that unlocked insights on key growth drivers, resulting in a 30% increase in decision-making accuracy—leading to a 20% boost in user acquisition and 15% revenue growth.Tackling inefficiencies, I automated data processing between AWS-S3 and Snowflake using AWS Glue and Airflow, improving processing efficiency by 20%. I then developed a Python-based ETL workflow for risk metrics reporting, boosting reporting efficiency by 30% as the results were hosted on Splunk, allowing the operations and BI teams to act faster. My passion for tackling fraud also came to life when I built an alert-triggering platform and a Splunk dashboard to monitor incident severity, increasing fraud detection by 30% and saving the company $250,000 per quarter.When working with product owners, I analyzed Snowflake and MicroStrategy data to assess development trends, which led to a 25% increase in user growth as we prioritized long-term growth strategies. On top of that, I defined operational metrics and created over 100 Tableau reports, increasing efficiency by 15%, and streamlined workflows by 20% using Alteryx to automate ETL processes. Through these efforts, I not only saved time but ensured data accuracy and faster insights, driving the business forward.