At Lyft, community is what we are and it’s what we do. It’s what makes us different. To create the best ride for all, we start in our own community by creating an open, inclusive, and diverse organization where all team members are recognized for what they bring.

　　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, building, and launching 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.

　　

Responsibilities:

　　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

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

Benefits:

Extended health and dental coverage options, along with life insurance and disability benefits Mental health benefits Family building benefits Access to a Health Care Savings Account In addition to provincial observed holidays, team members get 15 days paid time off, with an additional day for each year of service  4 Floating Holidays each calendar year prorated based off of date of hire 10 paid sick days per year regardless of province 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible Lyft proudly pursues and hires a diverse workforce. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy.  Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process.  Please contact your recruiter now if you wish to make such a request.

　　This role will be in-office on a hybrid schedule following the establishment of a Lyft office in Toronto — Team Members will be expected to work in the office 3 days per week on Mondays, Thursdays and a team-specific third day. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year.