Data Engineer Manager

Lead, design, and scale data solutions to support Journey Analytics initiatives, with a strong focus on code quality, reusability, and reliable data platforms. This role is responsible for setting the technical direction, overseeing the evolution of data architectures, and leading a team of data engineers to deliver high-quality, performant datasets for analytics and reporting use cases.

The ideal candidate combines strong hands-on data engineering expertise with people leadership experience, and has a proven track record of driving scalable solutions in cross-functional environments.

Responsibilities

- Lead and 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 the 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 the 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.

- 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).

- Certifications in AWS (we are AWS Partners), Databricks, and Snowflake. - 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. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.