Este perfil está escrito en inglés
Data Analyst
Demonstrated experience with A/B testing.
Demonstrated experience with Hypothesis testing.
Demonstrated experience with Confidence intervals.
Demonstrated experience with KPI monitoring and analysis.
Demonstrated experience with User behavior analysis.
Data Analyst open to new opportunities
Analyzed user engagement, feature performance, and conversion metrics across web and mobile products; investigated significant changes and anomalies.. Evaluated A/B tests of search, discovery, and recommendation features, using statistical tests, confidence intervals, and randomization checks to compare variants.. Analyzed user journeys and funnels from searches and result views to clicks and exports; identified drop-offs and differences between platforms.. Built and maintained Looker dashboards to monitor and analyze product KPIs, sharing findings with product managers and cross-functional teams.. Queried and processed large datasets using SQL, Python, and PySpark in Databricks, working with Delta Lake, Parquet, and AWS S3.. Validated analytics events, investigated instrumentation inconsistencies, and worked with engineers to improve reporting reliability.
Collaborated with departments on software requirements and technical documentation.. Designed and maintained SQL Server databases, including tables, views, indexes, stored procedures, functions, triggers, and roles.. Developed desktop applications in Visual Basic 6.0 and produced business reports, charts, and visualizations using Crystal Reports.. Built and maintained ASP.NET Core web services with REST-style CRUD operations and IdentityServer4 integration.
Managed computer accessory sales to partner shops and supported sales promotions.. Reviewed sales reports to identify business trends and inform inventory oversight.. Coordinated with software developers to support operational and reporting needs.
I used to work closely with product managers, developers, and other team members. I regularly prepared analytical reports, documented any findings, and explained the results of A/B tests. I also used Jira and Confluence to document requirements, report data-related issues, bugs, and share important information with the team. I believe that clear written communication helps avoid misunderstandings.
If the instructions for an AI training task were unclear, I would first carefully review them again to make sure I hadn't missed any important details. I would also check any available guidelines, documentation, or examples to better understand the requirements. If something was still unclear to me, I would contact my team lead or supervisor and ask for clarification, rather than making assumptions. I believe it is important to fully understand the instructions before starting a task, especially when working with AI training data, where accuracy and consistency are very important.
At my previous job as a data analyst, attention to detail was a very important part of my daily responsibilities. I worked with large datasets, calculated different metrics, analyzed results, and prepared reports for our team. I always paid close attention to data quality and accuracy. Before sharing any results, I carefully checked the data, verified any calculations, and made sure the numbers were correct and consistent. If I noticed any unexpected results or inconsistencies, I investigated them to understand the reason and corrected any errors before preparing the final report.