Rocio B.

I help businesses, startups, and operations teams turn fragmented data into reliable pipelines, automated reporting, and AI-ready data foundations. If your data is scattered across spreadsheets, databases, APIs, or operational systems—or your team is spending hours preparing reports manually—I can help you automate the process and build a practical cloud data foundation. How I Can Help ✔ Data Pipeline Development — Build reliable ETL/ELT pipelines for automated batch data workflows. ✔ Cloud Data Platforms — Design practical, cost-conscious data solutions using AWS, Microsoft Azure, and Microsoft Fabric. ✔ AWS Data Engineering — Build serverless data platforms with Amazon S3, AWS Glue, Athena, Lambda, Redshift, and CloudWatch. ✔ Data Integration — Connect APIs, databases, files, and operational systems into centralized, analytics-ready data. ✔ Analytics & Reporting — Prepare trusted datasets for Power BI, Amazon QuickSight, Tableau, and other BI tools. ✔ Data Quality & Observability — Implement validation, monitoring, alerting, and error handling to improve pipeline reliability. ✔ AI & Machine Learning Data Foundations — Prepare data for ML and Generative AI applications through data preparation, model-ready datasets, ML pipelines, and AI service integration. ✔ AI-Powered Data Operations — Apply Generative AI to practical workflows such as data quality analysis, anomaly investigation, incident triage, and operational automation. ✔ Data Architecture Reviews — Assess existing data workflows and identify practical improvements for reliability, scalability, performance, and cost. Technical Stack Cloud Data Engineering: AWS | Microsoft Azure | Microsoft Fabric AWS: Amazon S3 | AWS Glue | Athena | Lambda | Redshift | DynamoDB | Kinesis | SNS | SQS | CloudWatch | Amazon Bedrock Data Engineering: Python | SQL | ETL/ELT | Apache Spark | Apache Kafka | Data Lakes | Data Warehousing | Data Integration Microsoft: Azure Data Services | Microsoft Fabric | Azure Machine Learning | Data Factory | Delta Lake Analytics & BI: Power BI | Amazon QuickSight | Tableau | Streamlit | Grafana AI/ML: Amazon Bedrock | SageMaker | Azure Machine Learning | MLflow | XGBoost | Generative AI I specialize in building cloud data solutions that are practical, maintainable, and cost-conscious. I focus on using the right level of technology for the business—not adding unnecessary complexity simply because a more sophisticated architecture is available. My background in industrial operations and supply chain gives me an additional business perspective. I understand that data engineering is not only about moving data. It is about making operational information reliable enough to support decisions around costs, inventory, processes, customers, and performance. What You Can Expect ✔ Clear communication and well-defined project scope ✔ Clean, maintainable Python and SQL ✔ Practical architecture aligned with your requirements ✔ Data quality and monitoring considerations ✔ Clear documentation and technical handover ✔ Solutions designed with reliability, performance, and cost in mind Whether you need to automate manual reporting, integrate multiple data sources, build a cloud data platform, prepare data for AI/ML, or improve an existing pipeline, I focus on turning complex data problems into practical solutions your team can actually use. Send me your current data challenge, and let's identify the most practical path forward. #DataEngineer #DataEngineering #AWS #Azure #MicrosoftFabric #Python #SQL #ETL #ELT #DataPipelines #AWSGlue #AmazonS3 #AmazonAthena #AmazonRedshift #DataLake #DataWarehouse #DataIntegration #CloudData #Serverless #PowerBI #AI #MachineLearning