Real job — pulled straight from Jobgether’s careers page · Verified August 12, 2026 · No reposts.
Job description
Jobgether is hiring a Senior Data Engineer — a full-time, based in Brazil role. Apply directly on Jobgether's careers page below.
Engenheiro de Dados Sênior
Team: IT
Location: Brazil
Commitment: Full-time
Workplace Type: remote
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engenheiro de Dados Sênior based in Brazil.
As a Senior Data Engineer, you will play a key role in building reliable, scalable, and governed data ecosystems in a cloud-first environment. You will design and maintain robust ETL/ELT pipelines using Azure and Databricks, working with large volumes of data and modern engineering practices. The role combines data architecture, modeling, quality, governance, and cloud migration initiatives. You will also collaborate closely with business and data science teams to operationalize AI and machine learning models in the cloud. Strong attention to performance, security, automation, and data integrity will be essential to your success. You will work in a collaborative, technology-driven environment where AI, continuous learning, and innovation are part of everyday engineering practices.
Accountabilities:
- Develop, maintain, and optimize ETL/ELT data pipelines in Azure using Databricks for large-scale data processing and integration.
- Design and optimize data models to ensure scalability, performance, consistency, and effective governance across the data ecosystem.
- Apply a 360-degree perspective to technical decisions, understanding how individual solutions can affect broader architectures, projects, and business outcomes.
- Implement CI/CD practices to automate deployments and maintain reliable code versioning and delivery workflows using GitHub.
- Support cloud migration initiatives, ensuring data security, quality, integrity, and continuity throughout the migration process.
- Collaborate with business and data science teams to deploy AI and machine learning models in cloud environments.
- Develop and maintain data-quality practices, including data profiling, monitoring, validation, and consistency controls.
- Support the operationalization of machine learning models using Databricks and MLOps practices.
- Document data architectures, engineering processes, standards, technical decisions, and best practices.
- Contribute to the definition and implementation of data engineering standards, governance practices, and scalable architectural patterns.
- Support automation initiatives and continuously identify opportunities to improve data pipelines, processes, and engineering efficiency.
- Strong professional experience with Azure Data Services and Databricks.
- Hands-on experience using Databricks with PySpark for large-scale data processing and transformation.
- Experience with Medallion Architecture and modern data-platform patterns.
- Proven experience with MLOps and productionizing machine learning models in Databricks.
- Strong proficiency in Python and SQL for data manipulation, transformation, and engineering workflows.
- Experience implementing CI/CD practices and source-code versioning using GitHub.
- Solid knowledge of conceptual, logical, and physical data modeling.
- Experience participating in cloud migration projects, particularly involving data platforms and workloads.
- Strong background in ETL/ELT development and integration of large volumes of data.
- Experience deploying AI or machine learning models and working with tools such as Azure Machine Learning.
- Experience with AI projects and automated data-pipeline development.
- Strong understanding of data quality, governance, scalability, performance, and security principles.
- Ability to understand broader project impacts, work collaboratively with multidisciplinary teams, and communicate technical concepts effectively.
- Experience defining strategies, standards, and best practices for converting SAS code to Python/PySpark is a plus.
- Experience supporting the migration of SAS processes, routines, and analytical solutions to Databricks is desirable.
- Knowledge of SAS, Master Data Management (MDM), Databricks MLflow, Data Lakes, or Data Warehouses is an advantage.
- Willingness to work from the company's offices when residing in the Campinas Metropolitan Region, in accordance with the applicable attendance policy.
- Health and dental insurance.
- Meal and food allowances.
- Childcare assistance.
- Extended parental leave.
- Partnerships with gyms and health and wellness professionals through Wellhub/Gympass and TotalPass.
- Profit Sharing and Results Participation (PLR).
- Life insurance.
- Access to a continuous learning platform.
- Discounts through an employee discount club.
- Free online resources focused on physical health, mental health, and overall well-being.
- Pregnancy and responsible parenting courses.
- Partnerships with online learning platforms.
- Language-learning platform.
- Opportunities for continuous professional development in a technology-driven environment.
- Inclusive culture supported by dedicated health and well-being teams, inclusion specialists, and affinity groups.
- Collaborative environment with exposure to modern data, cloud, AI, and machine learning technologies.
Requirements
Benefits
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Frequently asked questions
What skills are required for Senior Data Engineer at Jobgether?
The required skills for Senior Data Engineer at Jobgether include: Azure, Databricks, Spark, Python, SQL, CI/CD, GitHub, ETL, Machine Learning, MLOps, SAS.
What is the seniority level for Senior Data Engineer at Jobgether?
Senior Data Engineer at Jobgether is a Senior level position.
How do I apply for Senior Data Engineer at Jobgether?
You can view the full description and apply for Senior Data Engineer at Jobgether on EchoJobs: https://echojobs.io/job/jobgether-engenheiro-de-dados-s-nior-76eme.