Development of ETLs and ELTs using Selenium, Airflow, Spark and DBT to acquire and process data from various sources, with logs processed and displayed on PowerBI dashboards with various metrics and KPIs. Automation of manual routines, with CI/CD pipelines integrated into Azure, orchestrating with Prefect, and integrating with AWS services for monitoring and cataloging. Development and maintenance of Delta Lakes with Iceberg, and construction of Data Warehouses and Data Lakes.Python(pandas, pyspark, polars, scikit-learn, h3, pytest, selenium, beautifulsoup), SQL, AWS (S3, Glue, Athena, RDS, Lambda, StepFunctions, RedShift), Azure (Functions, Events, Geoserver, Blob Storage), Terraform, Docker, DBT, Airflow, Prefect, ETL, ELT, APIs, RabbitMQ, Data Warehouse, Data Lake, Delta Lake, Data Modeling, Snowflake, Vertica, Neo4J, ElasticSearch, SQL Server, Salesforce, Data Profiling, Data Quality, Great Expectations, Data Literacy, Data Analysis, PowerBI, DAX, Postman etc.

• Development of a property registration flow using AWS Services, reducing registration failures by 70%.• Made improvements to Python applications that query data on products and customer behavior on a daily basis, reducing daily routines times by 25%.Optimized views and UDFs in Postgres, using execution plans and DMVs, reducing processing time and costs.• Development of a data acquisition and processing pipeline for use in the commercial area. Use of AWS Services and stored results in DynamoDB.AWS StepFunction, Lambda, Glue, SQS, S3, API Gateway, DynamoDB, VPC, EC2, Athena, Kinesis, Python, SQL, Postgres.