Supply chain · Production

TIER Mobility manages 350,000+ e-scooters and e-bikes across 560+ cities with dbt Cloud

The problem

As TIER Mobility scaled from a handful of cities to 560+ in four years, its data team became a bottleneck: data volume grew exponentially, the team expanded from 6 to 60 people, and the existing setup could not support the pace of contribution needed from all members.

Workflow diagram · grounded in source
1
Fleet allocation need
trigger
“TIER needs to know how many of their vehicles, and at what time, should be allocated to each of their tens of thousands of pick-up locations”
2
Dbt centralizes data models
integration
“dbt is the heart of our data stack. It all begins with centralizing and defining models and metrics in dbt”
3
Demand/supply prediction
ai_action
“Through demand prediction we operate in a much more efficient manner, unlocking profits of hundreds of thousands or even millions of dollars”
4
Fleet compliance monitoring
validation
“Fleet data backs our decisions on safety processes. It informs us if users are parking their e-scooters and e-bikes in the designated, compliant spots.”
5
City analytics for tenders
output
“TIER's city-focused analysts can build models on top of the existing data infrastructure to provide reliable data to cities—giving the company a competitive edge in tender processes”
6
User demand incentivization
output
“users are then incentivized to use idle vehicles and park them in areas with higher demand in exchange for free minutes”
Reported outcome

TIER scaled its data team from 6 to 60 people while increasing analyst output, onboards new members in one hour, completed a zero-downtime migration from Redshift to Snowflake in six weeks, and uses demand prediction to unlock hundreds of thousands or even millions of dollars in operational efficiency.

Reported metrics
Rides growth500x
Data team size growth with output increase6 to 60 data team members in under 2 years
New member onboarding time1 hour
Redshift to Snowflake migration timesix weeks
Show all 5 reported metrics
rides growth500x
data team size growth with output increase6 to 60 data team members in under 2 years
new member onboarding time1 hour
Redshift to Snowflake migration timesix weeks
demand prediction profit impacthundreds of thousands or even millions of dollars
Reported stack
dbt Clouddbt CoreRedshiftSnowflakeAirflowAWSEtleapGitHubLookerSegmentAmplitude
Source
https://www.getdbt.com/case-studies/tier-mobility
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

TIER scaled its data team from 6 to 60 people while increasing analyst output, onboards new members in one hour, completed a zero-downtime migration from Redshift to Snowflake in six weeks, and uses demand prediction…

What tools did this team use?

dbt Cloud, dbt Core, Redshift, Snowflake, Airflow, AWS, Etleap, GitHub, Looker, Segment.

What results were reported?

Rides growth: 500x; Data team size growth with output increase: 6 to 60 data team members in under 2 years; New member onboarding time: 1 hour; Redshift to Snowflake migration time: six weeks (source-reported, not independently verified).

How is this supply chain AI workflow structured?

Fleet allocation need → Dbt centralizes data models → Demand/supply prediction → Fleet compliance monitoring → City analytics for tenders → User demand incentivization.

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