Data Engineer Manager - AWS Databricks

Accountabilities:

• • Lead, mentor, and support a team of data engineers while remaining actively involved in development, architecture, code reviews, and technical decision-making.
  • Define and execute the technical strategy for Journey Analytics data platforms, balancing long-term architecture with practical delivery needs.
  • Design, build, and maintain scalable, automated data pipelines using Databricks, ensuring reliable downstream data consumption.
  • Develop modular and reusable data components that can support multiple customer or business journeys while reducing duplication and improving maintainability.
  • Design and evolve scalable data models for analytics, reporting, and future data use cases.
  • Manage and optimize GitHub repositories, promoting strong version-control practices, coding standards, documentation, and development workflows.
  • Lead and participate in refactoring legacy codebases to improve scalability, maintainability, performance, and reusability.
  • Establish and uphold standards for data quality, governance, performance, reliability, and engineering excellence across pipelines and datasets.
  • Collaborate with analytics, product, engineering, and other cross-functional stakeholders to align technical solutions with business priorities.
  • Identify technical risks, bottlenecks, and opportunities for improvement, taking ownership of mitigation and optimization initiatives.
  • Drive continuous improvement across data processes, engineering practices, automation, and documentation.

  Requirements:

  • 7+ years of professional experience in data engineering, with a strong track record of designing and delivering scalable data solutions.
  • Demonstrated experience leading or mentoring data engineering teams while maintaining strong hands-on technical involvement.
  • Hands-on expertise developing, maintaining, and optimizing data pipelines in Databricks.
  • Strong experience with GitHub repositories, Git workflows, version control, and collaborative software development practices.
  • Proven ability to refactor and maintain legacy codebases, improving their maintainability, scalability, and reusability.
  • Strong understanding of data modeling and the design of reusable data components for analytics and reporting.
  • Experience building reliable, scalable data platforms with a strong focus on data quality, performance, governance, and operational reliability.
  • Ability to work effectively across analytics, product, and engineering teams and translate business requirements into practical technical solutions.
  • Strong ownership mindset, with the ability to independently drive initiatives from high-level direction through execution with minimal supervision.
  • Strong problem-solving, communication, mentoring, and technical leadership skills.
  • Experience with AWS and cloud-based data engineering environments is valuable for this role.
  • Experience using Databricks Genie is considered a plus.

  Benefits:

  • Opportunity to lead and mentor a data engineering team while remaining hands-on with modern data technologies.
  • Exposure to large-scale Journey Analytics initiatives and complex, cross-functional data challenges.
  • Opportunity to influence data architecture, engineering standards, and technical strategy.
  • Hands-on work with Databricks, automated data pipelines, scalable data models, and modern development workflows.
  • A collaborative environment involving analytics, product, and engineering stakeholders.
  • Opportunities for continuous technical learning and professional development.
  • Ability to drive meaningful improvements in data quality, reliability, scalability, and engineering efficiency.
  • Confidential handling of candidate information in accordance with applicable equal employment opportunity guidelines.