Job Overview

Job Type

Full Time

Pay

Not disclosed

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Job description

Location:: United States
Work arrangement:: On-site

Role Summary

This is a remote position.

Do you enjoy technology, innovation and have agility in your DNA?

Then maybe you've just found your next challenge!

At Spassu, we have an incredible project underway — and it is ready to welcome professionals with that extra something.

If you are a Senior Data Engineer, have a restless profile, enjoy learning and want to grow together with a company that values new ideas, come with us!

Here, you will find space to expand your knowledge, apply your talent and be part of something truly meaningful, sign up and come and transform with Spassu!

MAIN ACTIVITIES: Responsible for the complete cycle of AI systems, working on development, support and support, creating applications, infrastructure and integrating AI into existing systems. Performs corrections, evolutions, adaptations and data migration, documents processes, extracts insights from data and trains teams.

Ingest, process and automate large-scale data flows (Pipelines and ETL);
Develop, monitor and optimize databases and data warehouses;
Implement access and protection policies for sensitive data;

Implement CI/CD (DevOps) tracks and collaborate with multidisciplinary teams.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Information Systems or IT-related areas.

Desirable experience: 05 years working in the area of information technology in development, systems maintenance, data analysis, database administration, or related areas; Experience in developing projects using AI (MachineLearning, DeepLearning and/or Generative AI); Proven practical experience in data engineering projects preferably in a corporate or industrial environment; Experience in programming languages such as Python, Java, Scala, and descripting tools for task automation.

Knowledge: Data Infrastructure (Big Data, SQL/NoSQL databases, cloud (AWS/Azure/GCP) and orchestration); Data Science and AI (Machine Learning Models, Generative AI, Python libraries and integration via APIs); Methodologies (Processing large volumes of data and agile Scrum/Kanban practices).