Pedro Henrique Santamaria Silva

Solutions Architect at Pipefy

Pedro Henrique Santamaria Silva has a strong background in sales and logistics. Pedro Henrique began their career at Ambev as a Logistics Intern, where they supported warehouse and inventory management KPIs and managed inventories of finished goods and packing materials. Pedro Henrique also created and automated KPI analysis and performance tracking dashboards. Pedro then transitioned to the role of Logistics Intern in Retail and Wholesale Distribution, where they continued to create and automate KPI analysis and performance tracking dashboards. Pedro Henrique participated in strategic meetings and was responsible for implementing key indicators. Most recently, Pedro joined Pipefy as a Sales Engineer, where they have been a Global Key Accounts Sales Engineer, utilizing their expertise to support global key accounts.

Pedro Henrique Santamaria Silva has a strong educational background focused on engineering and data science. Pedro Henrique completed their Bachelor of Engineering (BE) in Mechanical Engineering at Universidade Federal do Paraná, starting in 2015 and graduating in 2021. During their time there, Pedro developed a strong foundation in the principles of mechanical engineering.

In 2021, Pedro enrolled in USP - Universidade de São Paulo to pursue a Postgraduate Degree, specifically an MBA in Data Science and Analytics. This program is set to be completed by 2023, indicating Pedro's commitment to furthering their knowledge and skills in the field of data science.

In addition to their formal education, Pedro has obtained certifications to enhance their language proficiency. In 2012, they received a TOEFL ITP Certificate with a score of 620 out of 677, demonstrating their competence in English. In 2015, Pedro also earned a Duolingo English Test Certificate with a score of 97 out of 100, further highlighting their language fluency.

Overall, Pedro's educational history showcases their dedication to learning and professional development in the areas of mechanical engineering and data science.

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