Freelancer profile translated to English.

Description

Core technologies: Azure, Databricks, PySpark, SQL, AWS Glue, Fabric, Oracle, Power BI.

Data Engineer specializing in cloud solutions and data analytics with experience in Azure, Databricks, AWS, and Microsoft Fabric. I have participated in the design and implementation of Lakehouse architectures, ETL/ELT processes, pipeline optimization, and data platform migrations.

I can help you with projects such as:

Development and optimization of pipelines with PySpark, SQL, and Databricks

ETL/ELT processes with Azure Data Factory and AWS Glue

Data Lake / Lakehouse modeling and architecture

Data integration and workflow automation

Query optimization and performance tuning

Support for BI & Analytics initiatives (Power BI, semantic models)

I have worked in banking, insurance, and with large data volumes, participating in migration, data governance, observability, and DataOps projects.

If you need ad-hoc support, technical reinforcement for your team, or guidance on a data initiative, I can contribute at both the implementation and technical design levels.

Industry field of expertise

Languages

Spanish
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

BPT Software
Technical Lead
TECH
October 2025 - February 2026 (4 months)
Lima, Peru
•Workshops and technical advisory for clients on data and analytics initiatives in Azure/Databricks/Fabric.
•Design of data architectures for migration to Lakehouse environments, achieving approval from clients and stakeholders.
•End-to-end implementation of solutions in Azure and the analytical ecosystem: configuration of ADF (Linked Services, Integration Runtime), Key Vault, App Registrations, Service Principals, Storage Accounts, and VMs.
•Lakehouse implementation in Databricks: configuration of Unity Catalog (catalogs, schemas, permissions), credential management, and layer structuring (bronze, silver, gold).
•Integration with Microsoft Fabric: creation of semantic models and interoperability with Azure Storage and Databricks through Unity Catalog to maintain governance.
•Construction and governance of Data Lakes, ensuring organization, traceability, and information security.
•Designed and implemented integrations between AWS Glue and Databricks for data ingestion and transformation processes in Lakehouse architectures.
•Led/executed a Lakehouse platform migration in AWS, extracting data from OData with Glue Connections and DynamicFrames, enabling its registration in Glue Data Catalog, querying from Athena, and analytical consumption from Power BI.
PySpark SQL Microsoft Azure Amazon Web Services (AWS) Azure Databricks
INDRA
Technical Lead Databricks
BANKING AND INSURANCE
July 2024 - September 2025 (1 year and 2 months)
Lima, Peru
•Design and implementation of pipelines in Databricks + Azure Data Factory for payments/collections and source extraction.
•Modeling and implementation of the new collection flow for third parties; definition of data, rules, and controls.
•Performance and maintainability improvements: notebook optimization, standardization, and dependency management.
•DataOps artifacts: traceability, data dictionary, logbook, and incident handling workflow.
•Observability: event logs and alerts to Teams for success/warning/error; production support.
•Achievement: Production of the new collections flow for third parties.
•Achievement: Highest POCLAC score (BCP) in 5 months.
Azure Databricks Microsoft Azure PySpark Scala Microsoft SQL Server
Mi Banco
Data Deputy Manager
BANKING AND INSURANCE
October 2022 - February 2024 (1 year and 4 months)
Lima, Peru
•Evaluation of analytical platforms in AWS and Azure environments, considering services like Amazon Redshift and its Azure equivalent (Azure Synapse Analytics), through MVPs focused on performance, costs, and data governance.
•Design of analytical architecture in Azure (batch and real-time) for enabling a cloud data platform.
•Evangelization of guidelines and best practices to development teams and end-users (adoption and usage).
•Management and leadership of migration projects from sandbox to production (Risks/CRM): plan, budget, and risks.
•Definition of implementation methodology and refinement for risk mitigation (includes traceability).
•Data governance incorporated in cloud and on-prem projects; redesign of degraded processes and incident management.
•Achievement: Risks Migration: release of 10 TB and performance improvement of ~40%.
•Achievement: Top 5 in internal technology evaluation.
Microsoft Azure Azure Databricks PySpark Amazon Web Services (AWS) SQL