At Stefanini, I play a pivotal role in integrating on-premises databases into Azure-based ecosystems for TKElevator. Leveraging Azure Data Factory (ADF) pipelines, Databricks, and Power BI, I develop and maintain robust data workflows that ensure seamless and timely data availability to global stakeholders. In this position, I continuously refine and optimize stored procedures for key data domains Customer, Contracts, Elevator, Escalator, Service Ticket, Other Asset, and Unit Shutdown—improving data quality and ensuring accurate downstream reporting. I follow Agile methodologies to adapt quickly to evolving business requirements, documenting technical processes and parameterizing pipelines and Linked Services within ADF to support dynamic configurations, particularly for Nordic regions. My responsibilities also include cross-functional communication with various country teams and project managers, assessing integration needs, and proactively resolving data incidents. When certain records fail to appear in the Customer Portal, I identify and reprocess them in ADF, utilizing Databricks to transform and reconcile the data. Additionally, I incorporate CI/CD practices with Azure DevOps to streamline deployments, enforce version control, and enhance automation across data workflows. This hands-on approach ensures data integrity, reliability, and accessibility, directly supporting TKElevator’s strategic decision-making and operational excellence.

At Indra Company, I lead the end-to-end migration of on-premises databases to Microsoft Azure, utilizing Databricks (Spark SQL) and Python to process and transform large-scale datasets. I architect and maintain robust data pipelines with Azure Data Factory, ensuring seamless integration and efficient data flows. Additionally, I design and implement interactive dashboards using Power BI and Python, enabling real-time analytics and informed decision-making. Furthermore, I develop comprehensive data governance frameworks and documentation to uphold data quality, security, and compliance standards. To ensure scalability and reliability, I incorporate CI/CD practices with Azure DevOps, automating deployments, version control, and continuous integration across data pipelines and analytics solutions. This approach enhances delivery speed, reduces errors, and strengthens operational excellence in cloud data projects.

At Hexacta, I led the development and optimization of AWS Glue pipelines and Lake Formation configurations, leveraging PySpark for scalable data processing. Additionally, I engineered Databricks workflows in both Scala and Python, enhancing data reliability and implementing robust cloud monitoring solutions. I spearheaded the design of advanced query logic to support finance and NextGen integrations, and architected data pipelines using Azure Data Factory to streamline data ingestion and transformation. To drive data-driven risk management strategies, I developed interactive Power BI dashboards and integrated Python scripts for analyzing Customer Lifetime Value. I also established data governance frameworks for Business Intelligence and Finance domains, crafting HL7-compliant documentation and implementing best practices for data quality, security, and compliance.

At Encora, I focused on designing and optimizing AWS Glue pipelines that ingest and transform data from Amazon S3 using PySpark, significantly improving performance and reliability. I implemented finance-focused DataMarts in Snowflake, leveraging advanced SQL capabilities—including stored procedures—to drive data discovery and improve analytics efficiency. Additionally, I developed insightful dashboards using Microsoft Power BI and Tableau, enabling stakeholders to make data-driven decisions. I enhanced data processing and orchestration capabilities by refining Azure Data Factory pipelines and lake storage strategies. As a key contributor to cloud data warehouse solutions, I optimized Snowflake environments to meet evolving business and technical requirements. Beyond technical responsibilities, I engaged closely with business stakeholders, translating complex data concepts into actionable insights. I served in a business analyst capacity, interpreting business requirements, aligning them with technical solutions, and clearly communicating results to internal clients. This holistic approach ensured that technical implementations were consistently aligned with organizational goals.

At Indra Company (BBV A Project), I specialized in large-scale data ingestion and transformation, leveraging Control-M jobs, SparkSQL, and Spark Streaming to move critical data from host systems into a centralized data lake. Utilizing both Python and Scala, I performed complex distributed data processing on Hadoop-based environments, ensuring optimal data quality and performance. I integrated cloud-native solutions on Azure, developing data pipelines using Databricks, Azure Data Factory, and Azure Data Lake Storage. As a member of the Indra Big Data Datio Tribe for BBV A’s implementation project, I applied Agile methodologies, contributed to functional analysis, and maintained a problem-solving mindset to address evolving business requirements. In addition to core engineering tasks, I supported data visualization efforts with Tableau and Google Cloud Platform (GCP) tools, and conducted in-depth query analysis using PostgreSQL. Leveraging my understanding of business processes, I identified operational and strategic KPIs aligned with organizational objectives, ensuring that technical solutions consistently met business goals

During this period, I applied advanced data science techniques to conduct market research and evaluations, leveraging web scraping for data extraction and machine learning regression algorithms for predictive modeling. I utilized a range of Python libraries, including Scikit-Learn, Pandas, NumPy, TensorFlow, and Keras, to deliver robust data-driven insights. My work included performing clustering analyses, designing interactive dashboards, and developing efficient SQL queries, as well as creating stored procedures and functions to optimize data processing. I also reviewed and enhanced reporting solutions using SAP BusinessObjects Web Intelligence. In addition, I explored mobile development with Flutter, integrating Google Maps and Firebase to create innovative applications for the sharing economy. This diverse experience strengthened my ability to bridge technical solutions with real-world business needs and to adapt to a variety of data-centric technologies.

At Cabify, I managed and developed global reporting solutions across 13 countries, leveraging advanced data science techniques—such as Bayesian inference, linear and polynomial regression, k-means clustering, decision trees, random forests, survival analysis, and support vector machines—to optimize marketing campaigns and predict customer behavior. I designed recommendation systems that utilized discount program responses to identify heavy users, employing Pandas as an ETL and data processing framework and applying logistic regression to evaluate the impact of discounts on user engagement. To ensure the effectiveness of models, I measured algorithm performance using confusion matrices and ROC curves, refining approaches for maximum accuracy. Additionally, I conducted survival analysis in R and Python to estimate customer lifetime value (LTV), enabling targeted marketing strategies and improved retention efforts. By integrating Business Process Management (BPM) principles, I streamlined requirements gathering and reporting workflows, reducing development time and improving the precision of data-driven insights. Throughout my tenure, I provided both global and local marketing teams with actionable insights and supported strategic decision-making through clustering analyses and data-driven recommendations. Advising on user requirements (querying SQL databases)

At PSS Tecnologías de la Información (Banco Falabella), I managed the QA phase of a DataMart implementation focused on bank passive products. This involved creating and overseeing detailed project plans (Gantt charts), coordinating with ETL developers and software architects to ensure on-time delivery and high-quality outputs. I designed and executed QA tests by querying databases and refining ETL processes (Informatica PowerCenter), ensuring the integrity and reliability of data pipelines. Leveraging SAP BusinessObjects (Web Intelligence) and OLAP cube solutions, along with advanced SQL analytics, I delivered actionable insights through tailored reporting. I applied linear regression models to predict ATM failures, enabling proactive maintenance strategies, and developed Excel-based dashboards to enhance operational visibility and decision-making. Additionally, I implemented Business Process Management (BPM) methodologies to streamline ETL and database workflows, reducing inefficiencies and driving continuous process improvement throughout the data life cycle.

At Corporación Lindley (The Coca Cola Company), I redesigned and automated commercial operations processes, reducing process time by 40%. Leveraging advanced data science techniques—including Support Vector Machines, Decision Trees, Random Forests, and Neural Networks—I developed predictive models to forecast production batches, working closely with planning, marketing, and sales teams to select the most accurate algorithms. I utilized SAP BusinessObjects Dashboard 4.0, SQL queries, and OLAP cubes to deliver dynamic, customized reporting solutions. By connecting databases with Pandas-driven local transformations, I automated real-time reports that guided the sales force on product distribution for each point of sale. Through effective Business Process Management (BPM) and thorough process mapping, I introduced continuous improvements to data workflows. In addition, I employed clustering methodologies and linear regression to uncover new potential markets, achieving a 10% increase in sales compared to the previous period. This holistic approach integrated data engineering, advanced analytics, and process optimization to drive sustained business growth.

At SABMiller Latam (Colombia), I contributed to a 12% efficiency improvement in the Flash initiative by developing queries, dashboards, and automated data reconciliation processes. I applied data science methodologies to monitor and optimize processing times for outsourcing partners (Accenture, Tata Consulting Group, and K2), ensuring timely and accurate project deliverables. I supported the SAP deployment for the Flash project across multiple Latin American countries, utilizing SAP OLAP, Web Intelligence, Crystal Reports, and Universe Design to facilitate seamless data integration. Prior to SAP implementation, I conducted in-depth SQL Server data analyses to ensure data readiness and system reliability. In this role, I also managed benchmarking initiatives to identify improvement opportunities across Colombia, Peru, Argentina, Panama, Honduras, and El Salvador. By automating data cleaning and reporting processes, I delivered insights to the regional hub in Miami, enabling country managers to make informed decisions. Additionally, I implemented KPI-driven dashboards to uncover operational bottlenecks, achieving a 25% increase in efficiency, and established structured Business as Usual (BAU) frameworks to organize over 15% of activities and resources effectively. This collaborative, data-centric approach ensured alignment with functional leaders and streamlined the execution of strategic initiatives.