Data Engineering Solutions• Utilization of Azure Data Factory for building pipelines for ETL processes and for data integration from various sources SQL Server, MyQSL, CosmosDB• Employment of Python and SQL programming languages for pipeline development and advanced analytics.• Leverage of the Databricks platform for big data processing and advanced analytics, including data pipeline creation, ETL (Extract, Transform, Load) processes.• Implementation of Azure Synapse Analytics to store and manage massive data volumes. This involved data modeling, query optimization, and ensuring scalability.• Utilization of Hadoop for data processing and manipulation on on-premises servers.• Employment of Apache Spark for data processing in distributed environments.Retail Company Migration:• Migration of data from an on-premises server to the Azure cloud infrastructure.• Leverage of Azure Data Factory to construct ETL pipelines.• Outcome: Enhanced scalability, rapid scaling capabilities, and access to modern tools within the Azure cloud platform.Sales Performance Analysis for the Manufacturing Industry:• Integration of sales, production, and logistics data into a centralized data warehouse.• Development of sophisticated SQL queries to uncover sales patterns and supply chain bottlenecks.• Outcome: The company achieved production optimization and enhanced logistics efficiency.Financial Monitoring for a Law Firm:• Utilization of Databricks to process billing, expenses, and legal fees data.• Development of automated reports to monitor financial performance and pinpoint areas for improvement.

Azure Databricks:• Implement data pipelines, optimizing the ingestion, transformation, and analysis of large volumes of information.Data Analysis:• Perform exploratory data analysis to identify patterns, trends, and opportunities for improvement in business processes.Relevant Projects:Inventory Optimization (Retail Sector):• Developed Power BI dashboards to monitor product inventory across multiple stores.• Identified bottlenecks and suggested adjustments to reduce storage costs and prevent stockouts.Customer Segmentation (Financial Sector):• Hadoop for distributed processing• Migration of on-premises server and SQL database to Azure cloud • Classified customers into segments based on their purchasing behavior, enabling more targeted marketing campaigns.Sales Performance Analysis (Automotive Sector):• Created dynamic Power BI reports to track vehicle sales by model, region, and time period.• Identified market expansion opportunities and underperforming areas.

Development of Business Intelligence solutions:• Development of dashboards with strategic indicators for the commercial and service area related to agribusiness• Experience with SQL databases (SQL Server, MySQL, and PostgreSQL)• Creation of pipelines and analysis using Python and SQL languages• Responsible for the entire data area• Responsible for the entire process of database maintenance, ETL creation, creation and maintenance of BI reports• Contact with the board for assistance in decision-making and presentation of insightsDatabricks:• Advanced ETL: Use of PySpark Databricks for advanced analysis and data processing in large datasets.• Data Modeling: Development of multidimensional data models and OLAP cubes with SSAS to support efficient queries and analysis of large volumes of data.• Process Automation: Implementation of PowerShell for data process automation and use of SQL for data manipulation and querying.• Data Visualization: Creation of dynamic and interactive dashboards with Power BI to provide meaningful insights into important business metrics.