Data Engineer, Pricing
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Key skills for this role
About the Role
Lyft is seeking a Data Engineer to join the Pricing team, building scalable data pipelines that power real-time pricing, experimentation, and analytics. You will work with AWS, Kubernetes, and Apache Airflow to evolve data models and ensure data quality.
Key Skills for This Role
Responsibilities
- Own the pricing data pipeline, scaling data processing flow to meet rapid data growth
- 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)
- Perform 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 and external partners to achieve results
Requirements
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
- 2+ years of relevant professional experience
- Strong experience with Spark
- Experience with Hadoop ecosystem (S3, DynamoDB, MapReduce, Yarn, HDFS, Hive, Spark, Presto, Pig, HBase, Parquet)
- Strong skills in a scripting language (Python, Ruby, Bash)
- Good understanding of SQL Engine and ability to conduct advanced performance tuning
- Proficient in at least one SQL language (MySQL, PostgreSQL, SqlServer, Oracle)
- Experience with workflow management tools (Airflow, Oozie, Azkaban, UC4)
Full Job Posting
Company and Role Overview
- 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.
- Pricing is at the core of Lyft’s business, driving revenue, balancing supply and demand, and shaping user experience through real time and strategic decisions.
- As a Data Engineer on the Pricing team, you will help build the data foundation that powers Lyft’s pricing strategies.
Responsibilities
- Owner of the pricing 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
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
- 2+ years of relevant professional experience
- Strong experience with Spark
- Experience with Hadoop (or similar) Ecosystem, S3, DynamoDB, 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)
- Experience with workflow management tools (Airflow, Oozie, Azkaban, UC4)
- Comfortable working directly with data and business partners 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
- Child care and pet benefits
- Access to a Lyft funded Health Care Savings Account
- RRSP plan with company match to help save for your future
- In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy.
- Lyft supports new parents with 18 weeks of paid time off.
- Subsidized commuter benefits and Lyft ride credits
Workplace
- This role will be in office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays.
- Hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year.
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