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Simplr is a subscription-based marketplace that allows its customers to rent products, and then return them when they are done using them.

Headquartered in Barcelona, Spain, Simplr’s model promotes greater sustainability by eschewing the ‘buy-use-toss’ cycle that contributes to greater waste and excess manufacturing. Simplr allows users to ‘rent’ products like smartphones, furniture, and cars.

Data Ecosystem Engineer

“Resources are important, and with the work from Spaulding Ridge, we don’t need to expand our data team right now. Those resources can go to growing our content team, or our sales team, all while our data system still grows.”

Data Ecosystem Engineer, Simplr

Improve data reporting capabilities for better analysis

Simplr needed to improve their data reporting pipeline to drive faster and more accurate analysis to aid in decision-making for various parts of the business. The older software and data processes they used meant they needed help to improve data reporting speed, analysis, and modeling practices.

As a relatively young company, Simplr had recently built their data stack from scratch and wanted to create a firm foundation for data processes for the future, allowing them to grow within the capabilities of the new data solutions, instead of hurrying to implement new ones once they were at capacity.


Updating and upgrading legacy data system and data modeling process

Spaulding Ridge was brought in to implement data modeling best practices, enable Fivetran and DBT in place of a scheduled queries and self-made replication processes, and enhance and review a previous Looker project and provide Looker user training.

One of the first steps in the project was to migrate from a self-made replication process to Fivetran, improving data quality and pipeline performance. Spaulding Ridge helped Simplr overcome some initial confusions and configuration details. Everything was configured and working as plan within the set timeline.

In addition to the migration to Fivetran, the Spaulding Ridge team helped to migrate the data modeling process from the schedule queries process to a DBT model.

Finally, Spaulding Ridge reviewed the Looker project and highlighted best practices for the Simplr team. We provided business-user trainings to improve end users’ adoption of the tool and their ability to explore data in Looker.


Ready-made Looker training and best practice data modeling

Simplr was working with modest data engineering and analytical resources, so the new Fivetran system improved data quality and pipeline performance while relieving some of the maintenance burden on the data team. Likewise, the team expressed a minimal knowledge of DBT and limited bandwidth to learn it, so the Spaulding Ridge team’s expertise and implementation eliminated the need for additional resources and time spent by the Simplr data team.

The DBT model itself improved the data management and expanded modeling capabilities, again relieving maintenance burdens. Additional capabilities added included: environment management, testing, and snapshotting for back up of non-incremental sources.

Spaulding Ridge’s Looker training was particularly appreciated by the lead data engineer, who expressed that it would have otherwise taken weeks to months of time to create and deliver the training to the necessary users—time that is now better used for other key tasks.

Overall, the Simplr team felt that Spaulding Ridge’s contributions meant an improved, more autonomous data system that freed up money and resources to be better allocated elsewhere, empowering the company’s growth.


Data Ecosystem Engineer

“Right now, we at least have ten to fifteen people using Looker on a daily basis because of training that was provided by Spaulding Ridge. Without that, we would probably have to wait weeks or even months to create and execute our own training, given our limited bandwidth.”

Data Ecosystem Engineer, Simplr

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