Aarón Salinas Sánchez

Delivered critical data solutions for a 50+ person marketing and analytics department by partnering directly with the Product Owner to translate business needs into technical specifications. Engineered end-to-end data integration solutions for customer loyalty projects by defining requirements with cross-functional teams and implementing them in a cloud environment. Automated data pipeline creation by reengineering ETL workflows with a metadata-driven PySpark framework, reducing new source development time by 30%. Architected a new, end-to-end customer analytics data model, leading the complete redesign of core tables and establishing a new, optimized ETL process. Led a complex data migration project moving the primary data warehousing solution from Snowflake to Databricks; this initiative involved moving over 100 TB of historical data and dozens of critical pipelines, resulting in over a 50% reduction in annual data infrastructure costs while maintaining 100% data fidelity. Optimized and refactored the existing Google BigQuery data warehouse, enhancing ETL pipelines to significantly improve data quality and reduce job execution times. Managed large-scale data warehousing in Databricks using Delta tables, implementing multiple ingestion strategies including Change Data Feed (CDF), batch processing, and streaming pipelines to ensure both historical accuracy and real-time availability. Scheduled and orchestrated dozens of ETLs with Apache Airflow, maintaining complex DAGs that coordinated Databricks jobs, BigQuery data loads, and dbt transformations, ensuring reliability and scalability of the data platform. Mentored and coached 2 junior engineers, one of whom was promoted to a mid-level role within 12 months, fostering their technical growth through constructive code reviews, pair programming, and promoting best practices.

Designed and maintained scalable cloud data solutions on Google Cloud Platform (GCP) as a Cloud Engineer. Led the end-to-end development of a new data application, architecting the core processing logic in Java with Apache Spark and automating the entire supporting infrastructure on GCP including the Dataproc cluster using Terraform for a fully repeatable, production-grade deployment, achieving a 40% improvement in data processing thoughput. Designed and implemented new ETL processes for seamless data integration using dbt, BigQuery, and Dataflow, focusing on efficient data transformation logic within the data warehouse.

Developed and deployed robust ETL pipelines using PySpark to ingest data from diverse sources into a centralized data warehousing solution. Analyzed and optimized existing data pipelines, identifying performance bottlenecks and refactoring code to improve data processing speed and efficiency. Engineered real-time monitoring dashboards in Palantir Slate to provide supply chain managers with critical visibility into logistics and inventory levels. Collaborated with business stakeholders to define key metrics and data visualizations, translating complex supply chain data into actionable data insights.