Lead initiatives to migrate data from on-premises environments to cloud data lakes.
Design and implement robust, scalable data architectures aligned with industry best practices.
Develop and optimize ETL/ELT pipelines with a focus on efficiency, scalability, and governance.
Implement large-scale data processing solutions using Databricks and Apache Spark.
Ensure data quality, integrity, and security throughout migration and transformation processes.
Apply data governance and compliance best practices, ensuring adherence to corporate policies.
Collaborate with business teams, data scientists, and analysts, translating requirements into efficient analytical solutions.
Foster integration between technical and strategic areas, facilitating data-driven decision making.
Prepare and maintain technical and process documentation, ensuring traceability and consistency of solutions.
Contribute to the continuous evolution of the data architecture by proposing technological innovations and performance improvements.

Bachelor's degree in Computer Science, Software Engineering, or a related field.
Proven experience with Azure Data Lake, Databricks, and Hadoop ecosystem technologies.
Proficiency in programming languages such as Python, Scala, and SQL.
Strong experience with ETL/ELT tools and distributed data processing frameworks.
Advanced knowledge of relational and NoSQL databases, such as SQL Server and MongoDB.
Experience with data orchestration tools, including Azure Data Factory and/or Apache Airflow.
Familiarity with DevOps and CI/CD practices applied to data pipelines.
Knowledge of data governance, LGPD (Brazilian General Data Protection Law), and information security practices.
Experience with streaming architectures and real-time data processing.
Experience migrating data from legacy systems to cloud environments.
Technical certifications in Microsoft Azure, Databricks, or cloud data engineering.
Experience in multicloud environments (Azure, AWS, GCP).

Data Engineering Specialist I

Key skills