Willam F.
Data Analyst

Skills

Python

Mysql

Willam is available for hire

Hire Willam F.

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Bio

An experienced IT team manager specializing in high-performance Data Lake management and overseeing complex integrations between ERPs and Azure. Proficient in creating Layers, Pipelines, Stages, and tailoring Data Analytics, Data Quality, and Data Governance frameworks for large organizations across both public and private sectors. Possesses strong skills in database management using SQL and Postgres, alongside expertise in data visualization tools including Power BI, Tableau, and Qlickview. Adept in automation through Python and SQL Command Line, with a focus on optimizing and maximizing business profitability. A background in PPC and soft skills enhances capabilities in negotiating complex scenarios and effectively managing matrixed projects within agile methodologies.

BI Coordinator
1/2/2020 - 10/1/2023

Coordinated the implementation of the Data Lake environment using Azure's native tools, including Azure Synapse for end-to-end data management from ingestion to consumption. Leveraged Virtual Machines (VMs) as intermediaries for on-premise server integration and utilized Power BI for data visualization, while managing metadata with Azure's Data Catalog. Designed the architecture of the environment, including pipelines and workflows for daily ETL processes across transactional systems and Data Lake layers (Raw/Trusted/Refined/Stage/Analytics). Integrated the Data Lake with CRM systems using Pentaho, and managed data governance to ensure secure and restricted access to data. Facilitated data availability to business areas to enhance Sellin & Sellout revenue through campaign generation across Latin America.
Senior BI Analyst
8/2/2011 - 12/2/2019

Developed and implemented an integration solution for the Train Management System (GPV) with SAP to manage a high-value railway transportation contract. Elevated business performance by implementing Business Intelligence using Microsoft Power BI, resulting in agile, accurate, and reliable information. Maximized productivity of transportation flows (heavy haul and retail) and minimized tariff adjustment rate to 3.5%, generating savings of approximately R$ 50 million.