Full-Time

Data Engineer

Confirmed live in the last 24 hours

EvenUp

EvenUp

201-500 employees

Legal services for personal injury cases

Legal

Mid

Toronto, ON, Canada

Hybrid role requiring 2-3 days per week in the office.

Category
Data Engineering
Data & Analytics
Required Skills
Data Science
BigQuery
Elasticsearch
Data Analysis
Requirements
  • Extensive professional data engineering experience
  • Previous experience building out data warehousing, data pipelines, and internal analytics
  • Strong understanding and practical experience with data tooling, BI tools, and systems such as DBT, BigQuery, Elasticsearch
  • The ability to communicate cross-functionally with various stakeholders to derive requirements and architect scalable solutions
  • Have several years of industry experience building high-quality software, shipping production-ready code and infrastructure
  • You enjoy owning a project from start to finish and love to drive a project across the finish line
  • Interest in making the world a fairer place (we don’t get paid unless we’re helping injured victims and/or their attorneys)
Responsibilities
  • Democratize data at EvenUp. Ensure our organization can scale with consistent, standardized access to our data stores and accelerate our ability to build and experiment with data products
  • Architect and build out the future of data warehousing at EvenUp
  • Enable and empower our Data Science team to rapidly iterate on model experimentation
  • Design, organize and refine data storage strategies that reduce development friction for our tech organization
  • Collaborate with cross functional teams to solve critical data problems
  • Help grow our nascent Analytics team and define a “data first” mentality across our organization

EvenUp Law provides legal services focused on personal injury, trucking accidents, and medical malpractice. The firm operates in multiple states, including Texas, Pennsylvania, California, Indiana, and Georgia. Its approach involves detailed case preparation and argumentation, which has earned praise for being thorough and cohesive. EvenUp Law uses a contingency fee model, meaning they only receive payment if they win the case, taking a percentage of the settlement or judgment. What sets EvenUp apart from competitors is its ability to analyze cases like an adjuster or defense attorney, which enhances their preparedness during negotiations or trials. This method has led to higher settlement amounts and quicker resolutions for clients, reducing their stress. The firm also maintains a database of similar injuries and their values, which aids in providing accurate damage estimates.

Company Stage

Series D

Total Funding

$214M

Headquarters

San Francisco, California

Founded

2019

Growth & Insights
Headcount

6 month growth

18%

1 year growth

72%

2 year growth

520%
Simplify Jobs

Simplify's Take

What believers are saying

  • Raising $50.5 million in Series B funding, backed by prominent investors like Bessemer Venture Partners and Bain Capital Ventures, indicates strong financial health and growth potential.
  • The launch of Litty, an AI-powered assistant, showcases EvenUp's commitment to innovation and technological advancement in legal services.
  • High valuation at $325 million reflects market confidence and the firm's potential for significant impact in the legal tech space.

What critics are saying

  • The reliance on a contingency fee model means revenue is dependent on winning cases, which can be unpredictable.
  • Rapid growth and high valuation may lead to increased scrutiny and pressure to deliver consistent results.

What makes EvenUp unique

  • EvenUp leverages AI to transform raw files into legal documents, a unique approach in the personal injury sector.
  • Their meticulous case preparation, akin to that of adjusters or defense attorneys, sets them apart in achieving high settlement amounts and quick resolutions.
  • The firm's accurate and timely database of similar injuries and values enhances its negotiation capabilities, providing a competitive edge.

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