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Staff Data Engineer

At Lyft, our mission is to improve people’s lives with the world’s best transportation. To do this, we start with our own community by creating an open, inclusive, and diverse organization.


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.


You will report to a Data Engineering Manager.


Responsibilities:

  • Work across teams to gather requirements and drive alignment on project goals
  • Build and own the roadmap for Lyft’s Central Data team
  • Provide technical leadership to the team for executing projects within scope, time & quality constraints
  • Evangelize best practices within the team through tech talks, demos, show ‘n tells etc
  • Take initiative and craft projects to improve customer experience & satisfaction by resolving long standing problems or by building important user facing features
  • 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)
  • Design and develop framework to harden Lyft’s data infrastructure, in continue meeting regulatory complianceSQL and MapReduce job tuning to improve data processing performance


Experience:

  • 10+ years of relevant professional experience
  • Mentoring junior engineers through code reviews and project task breakdown, scoping and sequencing
  • Experience with Hadoop (or similar) Ecosystem (MapReduce, Yarn, HDFS, Hive, Spark, Presto, Pig, HBase, Parquet)
  • Proficient in at least one of the SQL languages (MySQL, PostgreSQL, SqlServer, Oracle)
  • Good understanding of SQL Engine and able to conduct advanced performance tuning
  • Strong skills in scripting language (Python, Ruby, Bash)
  • 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:

  • Great medical, dental, and vision insurance options
  • Mental health benefits
  • In addition to 12 observed holidays, salaried team members have unlimited paid time off, hourly team members have 15 days paid time off
  • 401(k) plan to help save for your future
  • 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
  • Pre-tax commuter benefits
  • Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program


Lyft is an equal opportunity/affirmative action employer committed to an inclusive and diverse workplace. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law. 

Company

Lyft

Location

San Francisco, CAOnsite

Job type

Full-Time

Category

Data Engineering

Tags

AirflowAzkabanData AnalyticsData pipelines
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