Lemonade is a technology-first insurance carrier that leverages AI and behavioral economics to provide renters, homeowners, car, pet, and life insurance. Founded in 2015, the company recently surpassed $1 billion in in-force premiums and has over 2.4 million customers throughout the US, UK, and Europe.
Lemonade uses Looker for business intelligence, GitHub for code management, and dbt for modeling and transformation. Before Foundational, their workflows relied on manual processes to trace data lineage.
When Lemonade was breaking into the insurance market, their focus was on speed and continuous innovation — building systems quickly and surfacing data fast.
As the company scaled, their infrastructure became more complex, with multiple systems managed by different teams, ranging from Product Analytics and Analytics Enablement to Operation Analytics and Financial Analytics. Today, Analytics at Lemonade, together with Data Engineering and Data Platform, exceeds 55 people across 4 teams.
Code reviews and pre-merge validations involved labor-intensive manual traces through the data lineage, making it “nearly impossible to determine who owned potential data issues,” notes Tal Kurnas, Lemonade’s Data Analytics Lead. Plus, contacting developers to find the source was a time drain.
“People would make changes without knowing if it broke something else,” adds Qun Wei, Lemonade’s VP of Data Analytics. As a result, small mistakes, if undetected, could cause significant build-time effects.
Working closely with Daniel Korn, Senior Director of Engineering, and Lemonade’s data platform team, Qun and Tal recognized the need for a more robust code validation process — one that offered end-to-end visibility into a disconnected tech stack and clarified how systems and data pipelines were interconnected.
During this time, Lemonade was planning to modernize their data infrastructure, replacing legacy core data and analytics systems with more streamlined platforms — all while keeping every part of the data stack in sync. The teams needed high visibility to proactively remove redundant assets and predict the effects of code or model updates. This way, they would minimize risk and speed up the migration.
That’s when they discovered Foundational.
“As we scaled, our data became increasingly complex. We had multiple systems managed by different teams, and limited visibility into how everything was connected, or even who owned specific issues.” – Qun Wei, VP Data Analytics
With Foundational, Lemonade has automated, column-level visibility across all data systems, including those outside the main data flow. Analysts, analytics engineers, and BI developers also get impact analysis and code validation that’s directly integrated into their GitHub and CI workflows. By integrating with GitHub, dbt, and Looker, Foundational parses code, analyzes logs, and extracts metadata, enabling developers to instantly trace data movement.
Foundational automatically conducts a build-time pull request analysis, scanning for dependencies. Critical code changes are approved once Lemonade reviews a clean impact assessment, which is automatically sent via Slack — removing the manual labor from code reviews.
Foundational’s fully automated data lineage and preventative data quality features enable developers from cross-functional teams to tackle dependencies together, identify data quality problems very early in the process, improve collaboration, and drastically reduce average cycle time.
The data lineage and governance tools have also made Lemonade’s migration project more seamless. Foundational clarifies complex data relationships and surfaces system dependencies, giving Lemonade a clear transition roadmap — reducing weeks of manual work to just days. With this level of visibility, Lemonade confidently identifies which legacy components to retain, update, or retire.
“Foundational’s ability to surface dependencies has transformed how we approach migrations and code reviews. Code review and data lineage tracking are now smooth processes that save us days of work.” – Qun Wei, VP Data Analytics
Foundational has given Lemonade’s teams end-to-end visibility across their data systems, enabling them to quickly trace dependencies, prevent build-time issues, and significantly reduce time spent on manual review and troubleshooting.
Looking forward, Lemonade’s growth ambitions — from onboarding new partners to exploring additional distribution channels and launching innovative products — all rely on accurate, accessible data to inform decisions. Foundational will continue to play a critical role in maintaining data clarity and trust, helping Lemonade scale quickly and confidently.
“Before Foundational, tracing issues or planning migrations could take days or even weeks. Having full visibility across our systems has transformed how quickly and confidently our teams can innovate, knowing every dependency and downstream effect is accounted for.” – Tal Kurnas, Data Analytics Lead
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