Mid Data Developer ( Azure + Databricks )

  This is an opportunity for a mid-level Data Developer to build and evolve modern, scalable data solutions in a technology-driven environment.
You will work across data engineering, distributed processing, cloud architecture, and data platform modernization.
The role focuses on developing reliable and efficient data pipelines designed for performance, resilience, and observability.
You will work extensively with Azure, Databricks, Apache Spark, PySpark, SQL Server, and Delta Lake to support large-scale data workloads.
The position combines hands-on development with DevOps practices, production monitoring, and continuous optimization.
You will also contribute to incident resolution, root cause analysis, and improvements to the reliability of production data environments.
If you are based in the Campinas Metropolitan Region, regular presence at local offices is required according to the applicable workplace attendance policy.

• Proven experience in data engineering, with hands-on experience developing and maintaining data pipelines and working with modern data processing technologies.
• Experience working in production environments, including monitoring, troubleshooting, incident investigation, and continuous improvement of operational data workflows.
• Experience handling large volumes of data and developing scalable processing solutions capable of meeting performance and reliability requirements.
• Experience participating in data migration, modernization, or data lake development projects, with an understanding of modern data platform architectures.
• Strong experience with cloud-based data environments, particularly Azure, and practical knowledge of Databricks for distributed data processing.
• Proficiency with Apache Spark and PySpark, including experience with data transformation, processing optimization, partitioning, and large-scale workloads.
• Experience with SQL and SQL Server, as well as knowledge of Delta Lake and data lake/data warehouse environments.
• Familiarity with Git and CI/CD practices, with the ability to apply DevOps principles to data engineering and deployment workflows.
• Strong analytical and problem-solving skills, particularly the ability to investigate incidents, perform root cause analysis, and implement sustainable solutions.
• Experience with AI projects and pipeline automation is considered an advantage, as is knowledge of data-oriented architectures and data governance best practices.
• Familiarity with Databricks MLflow for model management is a plus, particularly for professionals interested in the intersection of data engineering, AI, and machine learning.