Data Engineer, Experimentation

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.

At Lyft, data isn't just part of our decision-making process; it's the foundation. It drives our ability to deliver exceptional transportation experiences and offers insights into the impact of our product launches and features.

Joining Lyft as a Data Engineer means becoming a pivotal part of a team dedicated to shaping the future of transportation. You'll be tasked with developing robust data infrastructure—encompassing data transport, collection, and storage—and providing services that enable our leadership to make informed, risk-reducing decisions.

We are looking for a Data Engineer to join Lyft’s Experimentation and Metric Platform team. You will proactively propose new ideas, evaluate multiple approaches and choose the best one based on fundamental qualities and supporting data. Working along a talented team of cross-functional collaborators, you will architect, build, and launch scalable data pipelines to power result generation for all of Lyft's experiments and the computation of its core business metrics. The system you build will support the company’s growing data processing and analytics needs and allow access to business and user behavior insights, using huge amounts of Lyft data to fuel various teams such as Analytics, Data Science, Engineering, and many others. 

Our Data technology stack includes Airflow, Hive Metastore, Trino and Spark compute engines, and Python (for ETL). You will work with incredibly passionate and talented colleagues from software engineering, machine learning and data science on projects that delight millions of passengers and drivers.

Responsibilities:

• Architect and design data pipelines to power metric result generation for a suite of experimentation types, be 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 and adopt systems tracking data quality and consistency
• Propose and develop tools supporting self-service data pipeline management (ETL)
• Demonstrate familiarity with ML models productionization and server-side programming
• SQL and PySpark job tuning to improve data processing performance
• Build well-crafted, well-tested, readable, maintainable code with data infrastructure cost and scalability in mind 
• Participate in code reviews to ensure code quality and distribute knowledge
• Participate in on-call rotations to ensure high availability and reliability of workflows and data
• 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. 
• 3+ years of relevant professional experience
• Strong experience with Spark and SQL
• Strong skills in a scripting language (Python, Bash, etc)
• Experience with some of the following: Trino, Spark, Python, Airflow (orchestration), Hive Metastore, PostgreSQL, AWS services (S3, RDS, Elasticache, IAM)
• Experience building complex data models and pipelines
• 2+ years of experience with workflow management tools (Airflow or similar)
• Nice to have experience of working directly with cross-functional data analytics, data scientists, and engineering teams 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 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. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits

Lyft is committed to creating an inclusive workforce that fosters belonging. 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 if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. 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. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is $108,000-$135,000 CAD. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.