<p>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.</p><p>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.</p><h3>Responsibilities</h3><ul><li>Lead and mentor a team of data engineers, fostering best practices in coding, architecture, and data engineering standards.</li><li>Define and drive the technical strategy for Journey Analytics data platforms, ensuring scalability, maintainability, and performance.</li><li>Oversee the maintenance, optimization, and automation of code repositories in GitHub, ensuring high-quality and consistent development practices.</li><li>Guide the refactoring of legacy codebases to improve maintainability, scalability, and reusability across multiple use cases.</li><li>Drive the design and implementation of modular, reusable data components to support multiple journeys and reduce duplication.</li><li>Oversee the development and management of automated data pipelines in Databricks, ensuring reliability and scalability for downstream consumption.</li><li>Establish and enforce standards for scalable data modeling to support current and future analytics use cases.</li><li>Ensure data quality, governance, performance, and reliability across all data pipelines and datasets.</li><li>Partner with analytics, product, and engineering stakeholders to align data solutions with business needs and priorities.</li><li>Proactively identify risks, bottlenecks, and improvement opportunities, and drive mitigation strategies at a team and platform level.</li><li>Promote continuous improvement of data processes, documentation, and engineering practices.</li></ul><ul><li>7+ years of experience in Data Engineering.</li><li>Strong experience working with GitHub repositories and version control workflows.</li><li>Hands-on experience developing and maintaining data pipelines in Databricks.</li><li>Proven experience refactoring and maintaining legacy codebases.</li><li>Strong understanding of data modeling and reusable component design.</li><li>Experience building scalable data models for analytics and reporting use cases.</li><li>Strong focus on data quality, performance, and reliability.</li><li>Ability to work in cross-functional environments and contribute to continuous improvement.</li><li>Ability to work independently and take ownership of initiatives after receiving high-level direction, driving tasks forward with minimal supervision.</li><li>Experience using Genie (Databricks) (Plus).</li></ul><ul><li>Certifications in AWS (we are AWS Partners), Databric