ZenML Deploy to manage the MLOps pipelinesUsage of Kubeflow, Vertex AI, MLFlow to manage the MLOpsTerraform to deploy data processing infrastructure and data pipelinesMachine Learning creation engines to build interpretable modelsTables-based creation to apply the algorithms in an end-to-end data pipeline data source information to the userLed the implementation of the company's digital transformation process, focusing on data governance, DataOps, Analytics, Advanced Analytics, and Machine LearningExtensively utilized the Google Cloud Platform (GCP) Data Stack, leveraging key tools such as VertexAI, BigQuery, Bigquery ML, Artifact Registry, App Engine, Cloud Functions, Airflow (Composer), Dataflow (Apache Beam), Pub/Sub, and Git for code versioning (GitHub/BitBucket + CI)Created and managed end-to-end Machine Learning and ETL processes within the Data Lakehouse architecture, employing SQL and Python as primary languages. Developed data models to support consumption in Looker Studio and Metabase across all departments, ensuring comprehensive documentation and robust data management practices.Orchestrated data ingestion from transactional databases, employing hybrid platforms and conducting Change Data Capture (CDC) proofs-of-concept for seamless integration with BigQuery. Structured alternative ingestion methods to optimize data flow into BigQuery, aiming to establish a scalable and well-documented Analytics environment.Working with stack AWS + Trino + DBTExtras Works: Testing Snowflake Environment, Working on GCP + AWS Certifications

Conducted statistical metrics evaluation and customer segmentation initiatives to inform targeted marketing strategies, optimize business operations, and enhance performance in the healthcare sector. Leveraged diverse data sources, applied advanced analytical techniques, and developed optimization functions to drive operational efficiency and improve business processes.Behavior clusteringTime-series spike detection and non-linear detectionAlgorithms development to reduce the UCI hospitalization costs from search enginesServed as a data engineer specializing in developing Datalake and Business Intelligence (BI) solutions tailored to the healthcare sector. Collaborated on planning, defining business rules, and implementing innovative actions for SulAmérica's telemedicine initiatives.Utilized the Google Cloud Platform (GCP) Data Stack, leveraging key BigData tools such as BigQuery, Cloud Functions, Airflow, and the Git code versioning system (BitBucket + Jenkins CI).Spearheaded the creation and management of the end-to-end ETL process within the Datalake House concept, adhering to data governance protocols outlined in the DAMA-DMBOK Framework. Primarily employed SQL and Python to model data, ensuring seamless consumption in Looker Studio for the entire telemedicine domain.Assumed responsibility for ingesting data from diverse NoSQL databases, such as Firebase, Datastore, and MongoDB (DocumentDB), into BigQuery. Effectively structured and transformed information from unstructured fields, ensuring accurate representation within the analytics environment.

I developed Business Intelligence (BI) solutions focused on People Analytics as a Data Analytics Engineer. I collaborated on planning, defining business rules, and implementing innovative actions for the human resources area.Usage the Google Cloud Platform (GCP) Data Stack - including BigQuery, Kubernetes, Docker, API Engine, and Cloud Functions, to handle the Big Data paradigm.Leadership end-to-end ETL process using SQL and Python, transforming and modeling data for consumption in Power BI, in partnered with DataViz to create comprehensive Dashboards, integrating metrics and overseeing data processing. Managed Power BI Workspace, programmed executions, and implemented security rules such as Row-Level Security (RLS) Filter for public-facing applications.Deploying machine learning and Natural Language Processing (NLP) algorithms to analyze employee feedback during COVID-19, extracting meaningful insights.Deploying Natural Language Processing techniques and embracing MLOps principles, combining data ops and ML Engineer concepts to optimize machine learning workflows.

Development of dashboards and analytics processes using industry-leading Business Intelligence Software QlikSense and QlikView - such as Power BI, and Tableau.I built and maintained the end-to-end creation of ETL processes, leveraging SQL language embedded within Qlik Platforms. Data modeling emerged as a prominent skill, underscoring its significance throughout my career. Additionally, I specialized in building KPIs, defining business rules, and conducting analyses specific to the pharmaceutical industry. My experience encompasses a wide range of pharmaceutical industry audits, including nDDD, CUP, Audit, GPS, PMB, MDTR, and MI.I handled various data sources, frequently receiving working files in FLAT FILES from reputable providers like IQVIA or CloseUP. Additionally, I seamlessly integrated structured databases such as SQL Server and other sales control databases from client sources.Leveraged Alteryx Platform for data manipulation when necessary. Demonstrated versatility by spearheading a responsive Chatbot project utilizing QlikSense Server Engine, Python, and frameworks like RASA-NLU and Diagram Flow.