Engenheiro de Dados Sênior SAS

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 SAS based in Brazil.

This is a senior data engineering role focused on modernizing SAS-based data solutions and accelerating their transition to cloud-native architectures.
You will design, develop, and optimize scalable data pipelines across Azure and Databricks environments.
A key part of the role is translating SAS processes and analytical workloads into efficient Python and PySpark solutions.
You’ll contribute to cloud migration initiatives while ensuring data quality, security, governance, and performance.
The position involves close collaboration with business, data science, and technology teams, including AI-focused initiatives.
You’ll work with modern DataOps, DevOps, and MLOps practices in an environment focused on continuous innovation.
This is an opportunity to play a strategic role in transforming legacy data ecosystems into scalable, governed cloud platforms.

Accountabilities:

• Develop and maintain scalable ETL/ELT data pipelines in Azure, using Databricks for large-scale data processing and integration.
• Define and implement strategies, standards, and best practices for migrating SAS code and processes to Python/PySpark.
• Provide technical support for the migration and modernization of SAS routines, analytical processes, and solutions into Databricks.
• Read, understand, assess, and migrate existing SAS workloads while ensuring functional and data consistency.
• Design and optimize data models and architectures to support scalability, performance, maintainability, and governance.
• Work with medallion architecture principles across Data Lake and Data Warehouse environments.
• Implement CI/CD practices and code versioning using GitHub, supporting automated and reliable deployments.
• Support cloud migration projects while ensuring data security, quality, integrity, and operational continuity.
• Collaborate with business and data science teams to support the deployment of AI models and data solutions in cloud environments.
• Apply data profiling, quality checks, monitoring, and governance practices to maintain reliable and consistent datasets.
• Document data architectures, processes, migration strategies, technical decisions, and engineering best practices.
• Contribute to DevOps, DataOps, and MLOps initiatives and identify opportunities to automate and improve data pipelines.
• Support data ingestion and integration initiatives, including API-based integrations where applicable.

Requirements:

• Solid professional experience in data engineering, with strong expertise in Azure Data Services and Databricks.
• Hands-on experience with SAS, including the ability to read, understand, assess, and migrate SAS code and processes.
• Proven experience defining strategies and standards for converting SAS workloads to Python/PySpark.
• Strong experience supporting the migration of SAS analytical processes and solutions to Databricks.
• Hands-on expertise with Databricks and PySpark.
• Strong knowledge of Python and SQL for data manipulation, transformation, and analysis.
• Experience with medallion architecture, Data Lakes, and/or Data Warehouses.
• Solid understanding of ETL/ELT processes and integration of large data volumes.
• Experience with data modeling across conceptual, logical, and physical levels.
• Knowledge of CI/CD, GitHub, and automated deployment practices.
• Experience participating in cloud migration projects.
• Familiarity with DevOps, DataOps, and MLOps practices and workflows.
• Understanding of data governance and data quality principles in cloud environments.
• Strong analytical and problem-solving skills, with the ability to work across legacy and modern technology environments.
• Excellent collaboration and communication skills when working with business, engineering, data science, and technology stakeholders.
• Experience with AI projects and pipeline automation is a plus.
• Experience integrating data through SAP Ariba APIs is desirable.
• Knowledge of Hive and Teradata, particularly in legacy modernization initiatives, is an advantage.

Benefits:

• Health and dental insurance.
• Meal and food allowance.
• Childcare assistance.
• Extended parental leave.
• Partnerships with fitness and health professionals through Wellhub (Gympass) and TotalPass.
• Profit-sharing program (PLR).
• Life insurance.
• Continuous learning platform and professional development opportunities.
• Partnerships with online learning platforms.
• Language-learning platform.
• Discount club and partner benefits.
• Online platform focused on physical health, mental health, and overall well-being.
• Pregnancy and responsible-parenting courses.
• Inclusive workplace with dedicated health, well-being, and inclusion support.
• Opportunities to collaborate with multidisciplinary and international technology teams.
• Continuous exposure to cloud, data engineering, AI, and modern engineering practices.
• For professionals residing in the Campinas Metropolitan Region, office attendance may be required according to the applicable workplace policy.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1