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 is the only way we make decisions. It is the core of our business, helping us create a transportation experience for our customers, and providing insights into the effectiveness of our product launch & features.
As a Data Engineer at Lyft, you will be a part of an early stage team that builds the data transport, collection, and storage, and exposes services that make data a first-class citizen at Lyft. We are looking for a Data Engineer to build a scalable data platform. You’ll have ownership of our core data pipeline that powers Lyft’s top-line metrics; You will also use data expertise to help evolve data models in several components of the data stack; You will help architect, build, and launch scalable data pipelines to support Lyft’s growing data processing and analytics needs. 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, Marketplace, and many others.

EXPERIENCE:

• 3+ years of relevant professional experience
• Experience with Hadoop or similar Ecosystem (MapReduce, Yarn, HDFS, Hive, Spark, Presto, Pig, HBase, Parquet)
• Strong skills in a scripting language (Python, Ruby, Bash)
• Good understanding of SQL Engine and able to conduct advanced performance tuning
• Proficient in at least one of the SQL languages (MySQL, PostgreSQL, SqlServer, Oracle)
• 1+ years of experience with workflow management tools (Airflow, Oozie, Azkaban, UC4)
• Comfortable working directly with data analytics to bridge Lyft’s business goals with data engineering

• Owner of the core company 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
• Unblock, support and communicate with internal & external partners to achieve results