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Role Overview

Develop data pipelines (ETL/ELT);
Extract data from multiple sources (APIs, databases, files, etc.);
Clean, transform, and standardize data;
Model data for analytical consumption;
Create and maintain DAGs in Airflow;
Automate data workflows and schedules;
Monitor pipeline failures and performance;
Develop in Python (mainly using pandas and Spark);
Use containers with Docker;
Version control with Git;
Integrate with cloud environments (AWS, GCP, or Azure);
Translate business needs into data solutions;
Support areas such as finance, operations, and sales.

Requirements

Advanced knowledge of Python;
Airflow;
Docker;
Knowledge of SQL;
Experience with Pandas and Spark;

Tech Stack

Airflow
AWS
Azure
Cloud
Docker
ETL
Google Cloud Platform
Pandas
Python
Spark
SQL

Gentil Negócios

Open Positions (1)

Engineering

Data Engineer