Software Engineer, Data Engineering
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.
- Owner of the core company data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Lyft
- Consistently evolve data model & 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
Experience & Skills:
- Extensive 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, Perl, Bash)
- Experience with workflow management tools (Airflow, Oozie, Azkaban, UC4)
- Comfortable working directly with data analytics to bridge business requirements with data engineering
Lyft is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Lyft does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Pursuant to the San Francisco Fair Chance Ordinance and other similar state laws and local ordinances, and its internal policy, Lyft will also consider for employment qualified applicants with arrest and conviction records.
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