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 while actively contributing to development efforts and setting best practices in coding, architecture, and data engineering standards.
Define and drive the technical strategy for Journey Analytics data platforms, while remaining hands-on in the design and implementation of scalable solutions.
Actively contribute to the maintenance, optimization, and automation of code repositories in GitHub, ensuring high-quality and consistent development practices.
Participate directly in refactoring legacy codebases to improve maintainability, scalability, and reusability across multiple use cases.
Design and build modular, reusable data components to support multiple journeys and reduce duplication.
Develop and manage automated data pipelines in Databricks, ensuring reliability and scalability for downstream consumption.
Design and implement scalable data models to support current and future analytics use cases.
Ensure data quality, governance, performance, and reliability across all data pipelines and datasets through both oversight and direct contribution.
Collaborate closely with analytics, product, and engineering stakeholders to align data solutions with business needs and priorities.
Proactively identify risks, bottlenecks, and improvement opportunities, and take a hands-on role in driving mitigation strategies.
Promote continuous improvement of data processes, documentation, and engineering practices through both leadership and execution.
- 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.