Data Engineer Manager

Role Overview

• Lead, design, and scale data solutions to support Journey Analytics initiatives, with a strong focus on code quality, reusability, and reliable data platforms.
• Set the technical direction, oversee the evolution of data architectures, and lead a team of data engineers to deliver high-quality, performant datasets for analytics and reporting use cases.
• Mentor a team of data engineers, fostering best practices in coding, architecture, and data engineering standards.
• Define and drive the technical strategy for Journey Analytics data platforms, ensuring scalability, maintainability, and performance.
• Oversee maintenance, optimization, and automation of code repositories in GitHub, ensuring high-quality and consistent development practices.
• Guide the refactoring of legacy codebases to improve maintainability, scalability, and reusability across multiple use cases.
• Drive the design and implementation of modular, reusable data components to support multiple journeys and reduce duplication.
• Oversee development and management of automated data pipelines in Databricks, ensuring reliability and scalability for downstream consumption.
• Establish and enforce standards for scalable data modeling to support current and future analytics use cases.
• Ensure data quality, governance, performance, and reliability across all data pipelines and datasets.
• Partner with analytics, product, and engineering stakeholders to align data solutions with business needs and priorities.
• Proactively identify risks, bottlenecks, and improvement opportunities, and drive mitigation strategies at a team and platform level.
• Promote continuous improvement of data processes, documentation, and engineering practices.

Requirements

• 7+ years of experience in Data Engineering.
• Strong experience working with GitHub repositories and version control workflows.
• Hands-on experience developing and maintaining data pipelines in Databricks.
• Proven experience refactoring and maintaining legacy codebases.
• Strong understanding of data modeling and reusable component design.
• Experience building scalable data models for analytics and reporting use cases.
• Strong focus on data quality, performance, and reliability.
• Ability to work in cross-functional environments and contribute to continuous improvement.
• Ability to work independently and take ownership of initiatives after receiving high-level direction, driving tasks forward with minimal supervision.
• Experience using Genie (Databricks) (Plus).

Benefits

• Access to AI learning paths to stay up to date with the latest technologies.
• Study plans, courses, and additional certifications tailored to your role.
• Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
• English lessons to support your professional communication.
• Travel opportunities to attend industry conferences and meet clients.
• Career development plans and mentorship programs to help shape your path.
• Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
• Company-provided equipment.
• Flexible working options to help you strike the right balance.
• Other benefits may vary according to your location in LATAM.