At a Glance.



Data Engineering, QA, Software Engineering, Data modelling, Data Architecture, Solution design.


Car rental and mobility services.


Github, AWS, Databricks, MS Excel.


As the world's leading mobility provider with annual revenues exceeding eight billion, Hertz stands proud as the sole true global car rental company, offering quality services for over 90 years.

From car rentals to heavy equipment and tools for construction, Hertz provides reliable solutions. With low fixed prices, Hertz Car Sales offers one-year-old vehicles, ensuring customer advantage. Whether it's operational leasing, fleet management, or entertainment services, Hertz delivers quality, reliability, and worldwide service.

The Challenge.

As a core part of their data strategy, Hertz needed a modern, scalable data platform to underpin their global business applications allowing them to serve both current and future business needs.


Hertz had challenges with the previous partner they engaged to help them and engaged Spark’s help to drive the completion of the data platform and its deployment to production. The Enterprise Data team in Hertz approached Spark based on endorsements of our work from other parts of the Hertz organisation.


Hertz engaged Spark to add capacity and expertise to their data platform project after dissatisfaction with a previous vendor. Facing challenges in user identification and frequent team changes, Spark focused on implementing the medallion architecture using Apache Spark Streaming and AWS, with a specialized framework for data ingestion. The project’s main aim was to enhance data management and improve user feedback processes.

Spark Impact.

The main impacts the Spark team had on this project are:

  • Creating a framework that generates a graphical model view from the architect Excel metadata
  • Designed, implemented and owned the Silver II (Pits and Bridges) layer
  • Designed, implemented and owned a pipeline from their Excel models to usable pipeline metadata
  • Significantly accelerated deploying the data platform in prod and debugging difficult tech prod issues.
  • When no one else knew how to solve it - we did!