Data Engineer Manager

About this role

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.