Data Engineer

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. 

The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. 

As a Data Engineer on the SCC team, you will have ownership over the data modeling and pipelines that power SCC’s Associate and AI Agent Platform . Your efforts will be critical to the reliability of our pipelines, execution of third party data integrations, accurate reporting of agents performance, and efficiency improvements that can save millions of dollars / year. You will work cross-functionally to bridge Lyft's business goals with data engineering. Your efforts will allow access to business and user behavior insights, using huge amounts of Lyft data to fuel several teams such as Analytics, Data Science, Engineering, and many others. 

• →Owner of the core data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Lyft
• →Evolve data model and data schema based on business and engineering needs
• →Implement systems tracking data quality and consistency
• →Develop tools supporting self-service data pipeline management (ETL)
• →SQL and MapReduce job tuning to improve data processing performance
• →Write well-crafted, well-tested, readable, maintainable code
• →Participate in code reviews to ensure code quality and distribute knowledge
• →Collaborate cross-functionally with product, engineering, data science, and marketing teams to understand business problems and align on prioritization and solutions

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
• 4+ years of professional experience in data engineering, ideally with large-scale distributed systems.
• Strong skills in Spark, Python (or similar scripting language), and SQL performance tuning
• Experience with AWS, Hadoop/S3, Hive, Presto, Airflow, and related tools
• Solid understanding of ETL processes, workflow orchestration, and data warehousing
• A collaborative mindset—you’re comfortable working across teams to solve real-world problems