Data Engineer Manager

BrazilRemoteFull-timemid

Data EngineerData

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer Manager based in Brazil.

Technical Tools

Data EngineerData

This role provides technical leadership in the design, implementation, and evolution of modern data architectures and platforms. You will work on complex, large-scale data environments supporting AI and software engineering initiatives. The position combines hands-on engineering with architectural decision-making, technical guidance, and platform evolution. You will build scalable pipelines, establish data quality and governance practices, and optimize reliability, performance, and costs. The role requires close collaboration across teams and the ability to balance technical excellence with business and operational priorities. You will also help drive the use of AI throughout the data engineering lifecycle while mentoring engineers and strengthening team autonomy.

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

~2 min read

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.