Data Developer Sênior

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Developer Sênior based in Brazil.

This is an opportunity to work on modern data platforms that support large-scale digital transformation and AI initiatives. You will design, develop, and optimize data pipelines and models across cloud-based environments. The role combines hands-on engineering with a broader view of architecture, data quality, security, and governance. You will work extensively with Azure, Databricks, PySpark, Python, SQL, and both relational and non-relational databases. You will also contribute to cloud migrations and the deployment of AI and machine learning models into production. The environment values collaboration, continuous learning, technical ownership, and pragmatic problem-solving. For professionals based in the Campinas Metropolitan Region, regular presence in the local offices is required according to the applicable work policy.

Accountabilities:: Develop and maintain scalable ETL/ELT data pipelines using Azure Data Services and Databricks, ensuring reliable processing and integration of large data volumes.

Build data solutions using Databricks and PySpark, applying appropriate architecture patterns such as the Medallion Architecture.

Query, integrate, transform, and manage data across relational and non-relational environments, including SQL and MongoDB.

Design and optimize conceptual, logical, and physical data models with a focus on scalability, performance, consistency, and governance.

Implement CI/CD practices and automated deployment workflows, using GitHub for source control and versioning.

Support cloud migration initiatives while maintaining data security, quality, integrity, and operational reliability.

Collaborate with business and data science teams to deploy AI and machine learning models in cloud environments, including solutions supported by Azure ML and Databricks.

Contribute to MLOps practices and the automation of data and machine learning pipelines.

Monitor data quality and consistency through profiling, validation, and ongoing monitoring practices.

Document data architectures, processes, technical standards, and engineering best practices.

Apply a 360-degree perspective to technical decisions, considering how individual solutions may affect the broader project and ecosystem.

Contribute to the continuous improvement of data architecture, governance, and engineering standards.

Requirements

Solid professional experience in data engineering and development, with hands-on experience building and maintaining data solutions in cloud environments.

Strong experience with Azure Data Services and Databricks, including practical experience with PySpark.

Experience working with MongoDB and SQL for data querying, transformation, integration, and manipulation.

Strong proficiency in Python and SQL for data engineering and transformation activities.

Experience with ETL/ELT processes and integration of large volumes of data.

Practical knowledge of Medallion Architecture and modern data platform concepts.

Experience with data modeling across conceptual, logical, and physical levels.

Experience implementing CI/CD practices and code versioning using GitHub.

Experience supporting cloud migration projects, with attention to security, data quality, and integrity.

Experience with productionizing machine learning models in Databricks and applying MLOps practices.

Experience deploying AI or machine learning models in cloud environments, including familiarity with tools such as Azure ML.

Experience automating data and AI pipelines.

Strong understanding of data architecture, governance, scalability, and performance optimization.

Ability to take a holistic view of projects, anticipate dependencies, and understand the broader impact of technical decisions.

Strong collaboration and communication skills, with the ability to work effectively with technical, data science, and business stakeholders.

Familiarity with SAS is considered a plus.

Experience with Master Data Management (MDM), Data Lake, or Data Warehouse environments is an advantage.

Familiarity with Databricks MLflow for model management is desirable.

Benefits

Health and dental insurance.

Meal and food allowances.

Childcare assistance.

Extended parental leave.

Partnerships with gyms and health and wellness professionals through Wellhub and TotalPass.

Profit Sharing and Results Participation (PLR).

Life insurance.

Continuous learning opportunities through a dedicated learning platform.

Discounts through a corporate 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.

Additional benefits and development opportunities.

For professionals residing in the Campinas Metropolitan Region, office attendance applies according to the current 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