Florianópolis, Santa Catarina, Brazil
Work experience with projects, data analytics and data governance: - Work experience in Data Visualization ( Looker, Metabase, Power BI ); - Work experience in data analyses; - Work experience in ETL; - Work experience implementing a governed data lake at the cloud (GCP); - Knowledge in Data Quality; - Bachelor in Economics. I am always open to new challenges
1. Data Quality: Perform statistical and holdout analyses to ensure data accuracy and reliability. Develop and maintain ETL processes to manage and transform large datasets. Collaborate with various teams to align data initiatives with business objectives. 2. Driving Business Insights: Utilize SQL (Snowflake and BigQuery) for data extraction and manipulation to support data-driven decision-making. Conduct advanced data analysis to identify trends and generate actionable insights. 3. Automation and Efficiency: Implement Python scripts to automate repetitive tasks, increasing efficiency and reducing manual workload. Develop Python-based solutions to streamline data processing and reporting workflows. 4. Data Science Support: Contribute to data science projects, such as developing and optimizing machine learning models for prospecting campaigns. Apply machine learning techniques to enhance the effectiveness of marketing and sales strategies. 5. Data Visualization and Communication: Create visualizations to effectively communicate data insights to stakeholders and clients
1. Building commercial monitoring dashboards using Power BI and QlikView: • Develop and design interactive and visually appealing dashboards using Power BI and QlikView to monitor and analyze key commercial performance indicators. • Identify and track essential KPIs (Key Performance Indicators) to provide valuable insights on sales, goals, and overall sales team performance. 2. Creating automated reports (VBA): • Automate the generation of commercial reports using VBA to streamline the process of obtaining information, saving time and resources. • Customize reports to meet specific stakeholder requirements, ensuring clarity and relevance of the presented information. 3. Coding for business rule adaptation (Python + ETL): • Utilize Python and ETL techniques to adapt business rules to meet commercial needs, developing effective data analysis solutions. • Perform data transformations and manipulations to ensure data consistency, integrity, and quality for analysis purposes. 4. Querying and database construction: • Create complex SQL queries to extract and combine data from various sources such as databases, CRM systems, and spreadsheets to build consolidated databases. • Organize and structure databases efficiently, enabling in-depth analysis and data-driven strategic decision-making. 5. Data analysis and interpretation: • Conduct statistical and exploratory analyses to identify patterns, trends, and relevant commercial insights, providing support for strategic decision-making. • Interpret analysis results and communicate findings clearly and concisely to stakeholders, guiding commercial actions.
1. Elaboration of Power BI dashboards: • Design and create visually appealing and interactive dashboards using Power BI to provide comprehensive insights into after-sales performance, customer satisfaction, and service quality. • Identify key performance indicators (KPIs) and metrics relevant to after-sales operations and present them in a clear and actionable manner. 2. Database maintenance - Azure, SPSS: • Manage and maintain databases hosted on Azure and SPSS platforms, ensuring data accuracy, security, and accessibility for after-sales analysis and reporting. • Implement data governance practices to maintain data quality, including data cleansing, standardization, and validation. 3. Extraction and preparation of data (SQL): • Utilize SQL queries and data extraction techniques to retrieve relevant data from various sources, such as customer databases, sales systems, and service logs. • Cleanse, transform, and prepare data for analysis, ensuring data integrity, consistency, and compatibility across different datasets. 4. Tasks automation (Python and UiPath): • Develop automated workflows and scripts using Python and UiPath to streamline repetitive after-sales tasks, such as data extraction, report generation, and data updates. • Implement data integration and synchronization processes to ensure data consistency and accuracy across different systems and databases. 5. Advanced analytics and insights: • Apply advanced analytics techniques, such as predictive modeling and customer segmentation, to uncover hidden patterns, trends, and opportunities within after-sales data. • Generate actionable insights and recommendations to improve after-sales processes, enhance customer satisfaction, and optimize service delivery.
• Management reports using VBA and Power BI; • Maintenance of SQL databases;