Client
LADbible.
Summary
Following on from my role as Head of Software Engineering, I analysed the Data Science team to see how I could improve the effectiveness of the team across the company. Alongside a continuous culture shift piece, I worked with engineers and data scientists to ensure the architecture was first delivering the most value and second suitable to scale as the company evolved at speed. This project took a two-pronged approach, starting with the stabilisation of their ETL pipeline, to then planning out how the architecture could look in a cloud-based setup. I appointed a Lead Data Engineer to continue to lead the project in the direction we agreed upon collectively.
Josh came into LADbible first and foremost to keep me sane, though he might not have realised that at the time. I knew he could sort the technicals out and, of course, he did that without breaking a sweat. What I really needed after moving from a 25,000 employee corporate to a 180 person new media company was a sounding board to ensure that my reasoning and decision making was sound. We share a vision of culture and environment for technical excellence. I knew that I could trust him to help me build a new team that would work together and create a platform fit for the scale.
Mark Kitching, Head of Technology at LADbible Group
Highlights
- Migration of key reporting metrics to a new system to allow the company to review daily performance statistics and hourly health checks on editorial performance
- Recruitment and appointment of a Lead Data Engineer to take the team forward to continue to deliver value to the business through data insights and trends
- Matured and stabilised an ETL solution already in place by working with the team to decide on the best route forward (with an eventual plan to move to Google Cloud Platform)
- The internalisation of critical metrics which were capable with the existing architecture, saving the company tens of thousands in unnecessary subscription fees
- Executed a culture piece to raise awareness and capabilities of the Data Science team within the company. As a result of this, more people leveraged the data warehouse instead of using paid third party services