Engenheiro de Dados GCP (Analytics) Pleno

Accountabilities:: Develop and maintain scalable and efficient ETL/ELT data pipelines, ensuring that data flows reliably through ingestion, transformation, and delivery processes. Build data ingestion, transformation, and availability solutions within Google Cloud Platform environments, using appropriate cloud services to support analytics requirements. Design and implement distributed data processing solutions using Databricks and Spark/PySpark, focusing on performance, scalability, and maintainability. Design, evolve, and support modern data architectures, including Data Lake, Data Warehouse, and Lakehouse environments. Integrate data from multiple sources, including APIs, databases, and streaming platforms, ensuring consistent and reliable access to information. Establish and maintain practices that support data quality, governance, reliability, and consistency across pipelines and storage environments. Monitor cloud performance and optimize infrastructure and data workloads to improve efficiency and manage costs effectively. Collaborate with BI, Analytics, and Data Science teams to understand data needs and deliver reliable solutions that support analytical and business objectives. Requirements: At least 4 years of professional experience as a Data Engineer, with a solid track record of developing and maintaining data solutions. Practical experience with Google Cloud Platform, particularly services such as BigQuery, Cloud Storage, Dataflow, or similar technologies. Hands-on experience with Databricks and distributed data processing using Spark, including practical knowledge of PySpark. Strong proficiency in Python and SQL, with the ability to develop data processing solutions and query and manipulate complex datasets. Experience with relational and dimensional data modeling, including the ability to structure data appropriately for analytical use cases. Experience with pipeline orchestration tools such as Airflow or similar technologies. Familiarity with Git and code versioning practices used in collaborative software and data engineering environments. Experience with Delta Lake, Terraform, Docker, or data streaming technologies such as Kafka and Pub/Sub is desirable. GCP or Databricks certifications are considered a plus, as is experience designing Lakehouse architectures. Knowledge of BI and data visualization tools such as Power BI, Looker, or Tableau is a differentiator. Familiarity with data governance and LGPD requirements is valued, along with intermediate or advanced English proficiency. Strong analytical and problem-solving skills, with a focus on data quality, performance, scalability, and continuous improvement. Benefits: 100% remote work.