Engenheiro de Dados GCP Sênior (GCP / Databricks)

Accountabilities:: Design, implement, maintain, and evolve scalable data pipelines and architectures in Google Cloud Platform. Develop ingestion, transformation, and data availability processes using modern GCP data engineering architectures. Define technical standards, engineering best practices, and reusable frameworks for data pipeline development. Ensure the performance, reliability, observability, scalability, availability, and SLA compliance of data solutions. Develop and maintain analytical data models, including dimensional models, data warehouses, data lakes, and lakehouse architectures. Automate data integration and transformation processes while continuously identifying opportunities to improve efficiency and reduce GCP costs. Build and optimize solutions using technologies such as BigQuery, Dataform, Cloud Storage, Pub/Sub, Cloud Composer/Airflow, Dataflow, Cloud Functions, Cloud Run, Looker, and integrated BI tools. Implement Data Quality, Data Governance, and Data Observability practices, including monitoring, auditing, traceability, and automated validation. Ensure solutions comply with security requirements, LGPD, and applicable corporate policies. Support data platform migration and modernization initiatives, contributing to technical and architectural evolution. Lead strategic, tactical, and operational data engineering initiatives and participate in architectural decisions, technical reviews, and impact analyses. Mentor junior and mid-level data engineers while promoting high standards for development, testing, documentation, version control, and delivery. Contribute to technical roadmaps and innovation initiatives, identifying opportunities to improve the organization's data capabilities. Translate business requirements into scalable technical solutions and collaborate with Analytics, BI, Product, and other teams to prioritize initiatives and deliver business value. Requirements Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. At least 5 years of direct experience in Data Engineering with Google Cloud Platform. Strong hands-on experience with GCP services including BigQuery, Dataform, Cloud Storage, Pub/Sub, Dataflow, Cloud Composer/Airflow, IAM, Cloud Monitoring, and Cloud Logging. At least 3 years of professional experience working with Databricks. Advanced SQL and Python skills, with experience building robust data engineering solutions. Solid experience with ETL/ELT, data warehousing, dimensional modeling, data lakes, and lakehouse architectures. Knowledge of Shell Script and experience with Git-based version control. Experience with CI/CD, data testing, and automated data validation. Strong understanding of data governance, quality, observability, monitoring, security, and scalable cloud architectures. Strategic thinking, analytical ability, effective communication, proactive behavior, and strong problem-solving skills. Results-oriented mindset and ability to collaborate effectively across technical and business teams. Terraform, Docker, Kubernetes, Dataplex, Looker, MLOps, and event-driven architectures are valuable differentiators. Previous experience contributing to technical roadmaps, architectural discussions, mentoring, or technical leadership is advantageous. Benefits Full-time CLT employment. 100% remote work model in Brazil. Porto Seguro medical insurance, with the possibility of including spouse and children. Porto Seguro dental insurance for employees and dependents. Profit Sharing and Results (PLR). Childcare assistance. Alelo meal and food allowance. Home office allowance. Partnerships with educational institutions offering discounts and learning opportunities. Incentives for professional certifications, including cloud certifications such as GCP, Azure, and AWS. Livelo points program. TotalPass fitness benefit for employees and family members. Mindself support focused on meditation, mindfulness, and quality of life. Opportunities to work with GCP, Databricks, modern data architectures, cloud platforms, governance, observability, and data modernization. Exposure to technical leadership, architectural initiatives, mentoring, and cross-functional collaboration.