Data Specialist with expertise in extracting valuable insights from complex datasets and transforming this data into dashboards to assist in strategic decision-making. Responsibilities include contributing to the development of new products, setting operational goals, and enhancing production efficiency. Additionally, duties involve conducting studies on projections and market competitiveness.

This candidate is a seasoned professional specializing in cloud computing, microservices, and serverless architectures, with a strong proficiency in AWS technologies such as Lambdas, API Gateway, and AppSync. Demonstrated expertise includes leading the development of sophisticated systems like Evocities, an intelligent city management platform, and Evoview, a machine learning-based video analysis tool. Equipped with advanced knowledge in machine learning, this individual excels at integrating artificial intelligence into diverse projects, enhancing functionality and performance.

An experienced Analyst and Developer specializing in the design and implementation of workflows for efficient data collection and management.

Information Technology professional specialized in Data Analysis, currently progressing towards an MBA in Big Data and Competitive Intelligence and holding a postgraduate degree in Data Analysis. Serving as a Data Analyst with a seven-year background as a Business Intelligence (BI) Analyst, the expertise includes developing predictive, prescriptive, descriptive, and diagnostic analyses, alongside KPI indicators. Proficient in various visualization tools such as Power BI, QlikSense, MetaBase, Looker, and Tableau, dashboards and reports are effectively published to facilitate decision-making for stakeholders and the commercial team.

Paulo has six years of experience as a Machine Learning Engineer and two as a Backend Developer. His primary tech stack is Python, and he is proficient in machine learning algorithms, data analysis, and visualization.

Holds a degree in Computer Engineering from PUCPR, with over four years of experience in a Business Operations role specializing in Oracle SQL. Subsequently transitioned to a Business Analyst position within the oil sector, where responsibilities included working with SQL Server, Power BI, MS Azure, and implementing Scrum methodology. Additionally, managed a client portfolio, ensuring efficient Order to Cash process flow through SAP SD.

A dual citizen of Portugal and Brazil, possessing six years of professional experience as a computer engineer in the domain of business intelligence. Expertise includes the development of business intelligence solutions, dashboard creation, data manipulation, and system integrations, with a keen analytical approach and a passion for data.

Academic credentials include a Bachelor's degree in Computer Engineering from Metodista University São Paulo (2018) and a Postgraduate degree in Data Science with a focus on Big Data Analytics from Mackenzie University São Paulo (2020). Professional certifications encompass Tableau Specialist (ID 999441) and Google Analytics (ID 53844274). Proficient in Power BI, Tableau, and Google Data Studio, with a working knowledge of QlikView, QlikSense, and SpotFire. Achieved multiple Business Intelligence badges from Tableau, namely Tableau Analyst, Developer, Author, Consumer, Data Scientist, Designer, and Executive Sponsor.

Proficient in advanced English, with ongoing coursework to further language skills. General competencies cover system engineering, documentation, electronic devices, computer networks, software architecture, and calculus. Extensive experience with ERP systems, data-driven decision-making, programming logic, MIS, UML, business intelligence requirements gathering, data mining, statistical analysis, data strategy, data collection, big data processing, and a range of analytical tools, including Microsoft Excel, Tableau Desktop, Google Data Studio, Qlik, Tableau Prep, Talend, Pentaho Data Integration, Looker, Power Query, and Tableau Public.

Database expertise includes MySQL, Oracle 11g/12g, SQL Server 2008/2016, HP Vertica, Amazon Redshift (AWS), Amazon Athena, Hadoop, Azure, and Databricks.

A finance management graduate with a postgraduate degree in Applied Statistics, currently pursuing an MBA in Data Science and Analytics, possesses over a decade of expertise in data analysis and business intelligence. The career trajectory includes leadership roles that focus on improving operational efficiency through financial analysis of projects and budget development. Proficient in advanced statistical techniques, including multivariate analysis and machine learning methods such as Random Forest, Logistic/Linear Regression, NLP, and PCA. Recognized for significant contributions using analytical tools like Tableau, Power BI, and Python, while holding multiple professional certifications, including Project Management Professional and Lean Six Sigma Black Belt. Demonstrates a strong capacity for utilizing data-driven insights to enhance decision-making processes and foster continuous improvement across various organizational levels.

Yellowbrick Data is a data warehousing company that provides an advanced, high-performance data warehouse solution. It enables businesses to run complex queries on large datasets quickly and efficiently, supporting real-time analytics and business intelligence operations.

A Yellowbrick Data expert must have skills in SQL, data warehousing concepts, ETL processes, and performance tuning. They should also be proficient in using Yellowbrick's specific tools and interfaces, as well as have experience with database management and real-time analytics.

How to hire a Yellowbrick Data expert

Yellowbrick Data was founded in 2014 to address the need for faster and more efficient data warehousing solutions. It aimed to help businesses handle large volumes of data and perform complex queries quickly, enabling real-time analytics and improved decision-making.

About Yellowbrick Data

Strengths of Yellowbrick Data included high performance, scalability, and real-time analytics capabilities. Weaknesses involved potential high costs and complexity in deployment. Competitors included 

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