Led scalable enterprise data platforms combining Python, Spark, SQL, and cloud automation. Architected scalable ETL/ELT pipelines using Python, Apache Spark, SQL, and Airflow, accelerating multi-source enterprise data availability by 38% for analytics workloads. Designed cloud Data Lake and Data Warehouse architectures across Azure and AWS, improving trusted data access, scalability, and analytical query performance by 35%. Built Spark-based transformation workflows processing structured and semi-structured data, cutting batch runtime 40% while strengthening schema consistency and lineage across platforms. Optimized SQL queries, partitioning strategies, and data models, reducing warehouse response times 37% across high-volume reporting and decision-support workloads for enterprise teams. Implemented automated validation, monitoring, recovery, and data quality controls, reducing pipeline failures 32% and improving production dataset reliability for business users. Integrated relational, NoSQL, API, and streaming sources through Kafka and batch ingestion, expanding governed data coverage across enterprise workflows and analytics products. Partnered with data scientists, analysts, developers, and stakeholders to translate business requirements into scalable, documented, and reusable data products for decision making. Strengthened DataOps practices with Git, CI/CD, containerized deployments, and technical documentation, shortening release cycles 28% across distributed engineering teams in Mexico and Montenegro.

Built dependable operational data foundations for analytics, reporting, automation, and informed decisions. Developed Python and SQL ETL workflows integrating operational systems, improving reporting freshness 30% and reducing repetitive manual preparation for analysts. Designed normalized and dimensional data models for relational warehouses, increasing reporting accuracy 34% and supporting reliable trend analysis across departments. Optimized SQL queries, stored procedures, indexes, and extraction logic, reducing processing latency 35% for recurring operational dashboards and data exports. Automated data validation and exception handling across reporting pipelines, lowering recurring quality issues 29% and improving trust in management metrics. Documented source mappings, transformation rules, business definitions, and recovery procedures, enabling faster onboarding and more consistent cross-team data operations during releases.

Developed reliable data workflows supporting remote enterprise applications, reporting, and integration. Created SQL-based data extraction and transformation workflows, improving operational report accuracy 27% while supporting reliable remote application delivery and maintenance. Implemented data cleaning, validation, and reconciliation routines, reducing inconsistent records 25% across business applications, reports, and scheduled processing jobs for users. Collaborated with senior engineers on Git workflows, code reviews, debugging, and documentation, improving release quality for data-heavy enterprise modules across remote teams.

Modernized public-sector data operations through ERP automation, governance, and structured reporting. Built ERP data models, automated reporting pipelines, and governed workflows, reducing manual administrative processing 40% across regional and municipal organizations. Improved data accessibility through centralized SQL repositories, validation rules, dashboards, and documented processes supporting long-term operational decision-making and compliance requirements.