Gabriel Martelloti Email & Phone Number | Work experience with data projects, working as a business analyst, data analyst, data scientist, ML engineer and now data engineer; - Work experience with SQL (Snowflake, PostgreSQL, MySQL, Aurora, Redshift, Athena...) - Work experience with Python (pandas, numpy, seaborn...); - Work experience with Amazon Web Services (currently with the AWS Solutions Architect profesional certification); - Work experience with data vizualization tools such as Tableau, Looker; - Work experience with in-depth data/statistical analyses; - Work experience with applying supervised, unsupervised and recommending ML systems; - Work experience with applying ML models in production using MLFlow, gflow and CI/CD systems; - Work experience with iaac (Terraform); - Work experience with Docker and docker compose; - Work experience with Airflow; - Work experience with dbt;

Gabriel Martelloti

Work experience with data projects, working as a business analyst, data analyst, data scientist, ML...
Curitiba, Paraná, Brazil

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Gabriel Martelloti’s Email gabriel.martelloti@ebanx.com

Gabriel Martelloti’s Phone Numbers

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Gabriel Martelloti’s Location Curitiba, Paraná, Brazil

Gabriel Martelloti’s Current Industry Blue Orange Digital

Gabriel Martelloti’s Prior Industry Globo | James Delivery | Ebanx | Construtora Serra Da Prata

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Data Engineer

2022-01-01 00:00:00 - Present

Data Scientist

2021-07-01 00:00:00 - 2022-01-01 00:00:00

Senior Data Scientist

2020-11-01 00:00:00 - 2021-07-01 00:00:00

Data Scientist

2019-07-01 00:00:00 - 2020-02-01 00:00:00

Intern

2015-06-01 00:00:00 - 2015-11-01 00:00:00

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Gabriel Martelloti is a Work experience with data projects, working as a business analyst, data analyst, data scientist, ML engineer and now data engineer; - Work experience with SQL (Snowflake, PostgreSQL, MySQL, Aurora, Redshift, Athena...) - Work experience with Python (pandas, numpy, seaborn...); - Work experience with Amazon Web Services (currently with the AWS Solutions Architect profesional certification); - Work experience with data vizualization tools such as Tableau, Looker; - Work experience with in-depth data/statistical analyses; - Work experience with applying supervised, unsupervised and recommending ML systems; - Work experience with applying ML models in production using MLFlow, gflow and CI/CD systems; - Work experience with iaac (Terraform); - Work experience with Docker and docker compose; - Work experience with Airflow; - Work experience with dbt; - Work experience with Kubernetes;