Data Engineer Manager
J
JobgetherSecurity & IT
BrazilFull-TimeManager
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLCloud ComputingETLData engineeringSparkCI/CD
Requirements
- Degree in Computer Science, Information Technology, Engineering, or a related field.
- Strong professional experience in Data Engineering, ideally within complex, large-scale environments.
- Experience acting as a technical reference, contributing to architectural decisions and supporting the evolution of engineering teams.
- Advanced knowledge of SQL, Python, and Spark.
- Experience developing data solutions for Machine Learning, Generative AI, or agent-based systems.
- Experience working with modern data stacks in cloud environments.
- Strong background in data pipelines, ETL/ELT processes, distributed data processing, and integration across multiple data sources.
- Solid understanding of data architecture, scalability, security, performance, operational reliability, and cost optimization.
- Experience applying CI/CD practices to data engineering solutions.
- Knowledge of relational and dimensional data modeling.
- Experience using AI within software development and data engineering workflows.
Responsibilities
- Lead the technical design, implementation, and evolution of scalable data architectures and platforms across complex environments.
- Design, develop, and maintain high-performance, resilient data pipelines and ETL/ELT processes supporting large-scale data workloads.
- Define architecture standards and best practices for Data Lake, Data Warehouse, and Lakehouse environments.
- Implement Data Quality, observability, and reliability practices across data pipelines and platforms.
- Develop and evolve CI/CD pipelines and engineering practices for data processing and ETL/ELT solutions.
- Define and implement relational and dimensional data models aligned with analytical and operational requirements.
- Establish Data Governance practices, including data cataloging, lineage, access controls, and documentation.
- Apply FinOps principles to monitor, manage, and optimize the costs associated with data solutions.
- Leverage AI throughout the data engineering and software development lifecycle to improve productivity, quality, and delivery efficiency.
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