At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
Here at Lyft, Data powers all the decisions we make. It is the core of our business, helping us create a great transportation experience for our customers and providing insights into the effectiveness of our services and products.
This team focuses on supporting our business by building the data transport, collection, and storage that powers our Lyft Urban Solutions business. We are looking for a Data Engineer to build scalable solutions, leveraging their data expertise and our technology stack to provide timely, accurate data for our internal and external customers. You will have the opportunity to build and scale the data of our global micromobility platform. This role will involve collaborating with product managers, external stakeholders, GMs, engineers, and data scientists to gather and translate requirements into solutions, ensuring that data-driven decisions are at the core of our business.

EXPERIENCE:

• 3+ years of relevant professional experience in data engineering or a related field
• Strong expertise in SQL and experience with Trino or Spark/PySpark
• Experience building and optimizing complex data models and pipelines with a strong understanding of ETL processes
• Expertise in data modeling and ETL, with experience developing and optimizing complex data models and pipelines
• Hands-on experience with workflow management tools (e.g., Airflow or similar)
• Proficiency in a scripting language like Python or Bash
• Comfortable working directly with cross-functional teams (data analytics, data science, engineering) to align solutions with business goals

• Owner of core company data pipelines, responsible for scaling up data processing flow to meet the rapid data growth at Lyft
• Continuously evolve data models and schemas based on business and engineering requirements
• Implement and maintain systems to monitor and enhance data quality and consistency
• Develop tools that support self-service management of data pipelines (ETL) and optimize data processing performance
• Write well-crafted, well-tested, readable, maintainable code that prioritizes scalability and cost efficiency
• Conduct code reviews to ensure code quality standards and facilitate knowledge sharing
• Participate in on-call rotations to maintain high availability and reliability of workflows and data pipelines
• Collaborate with internal and external partners to remove blockers, provide support, and achieve results