Data Engineer Manager

Accountabilities:: 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. Work with different teams to ensure data integrity, quality, security, availability, and operational reliability. 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. Document architectures, data flows, processes, technical decisions, and relevant engineering standards. Troubleshoot complex technical issues, conduct root-cause analyses, and drive sustainable solutions. Apply FinOps principles to monitor, manage, and optimize the costs associated with data solutions. Act as a technical reference for the engineering team, supporting technical decisions, knowledge sharing, autonomy, and professional development. Leverage AI throughout the data engineering and software development lifecycle to improve productivity, quality, and delivery efficiency. 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. Ability to evaluate technical solutions from multiple perspectives, including scalability, security, performance, cost, and long-term sustainability. Experience with multiple cloud providers is a plus. Experience with Databricks or equivalent data platforms is a plus. Certifications related to data, cloud, or distributed processing are a plus. Knowledge of Data Lake, Data Warehouse, and Lakehouse architectures is a plus. Experience with Data Quality, observability, data reliability, cataloging, lineage, and governance tools is a plus. Knowledge of FinOps applied to data ecosystems is a plus. Experience working with international projects or distributed teams is a plus. Benefits: Health and dental insurance. Meal and food allowances. Childcare assistance. Extended parental leave. Partnerships with gyms and health and wellness professionals through Wellhub (Gympass) and TotalPass. Profit Sharing and Results (PLR). Life insurance. Continuous learning platform with access to professional development resources. Discounts through partner programs. Free online platform focused on physical health, mental health, and overall well-being. Pregnancy and responsible parenthood courses and support. Partnerships with online learning platforms. Access to language learning platforms. Remote work environment. Additional benefits according to the applicable employment and company policies.