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Data Engineer, Experimentation

At Lyft, our purpose is to serve and connect. To do this, we start with our own community by creating an open, inclusive, and diverse organization.

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
  • 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, color, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offenses, 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 in Toronto, Canada — 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.

The expected base pay range for this position in the Toronto area is $98,000 - $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.

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Average salary estimate

$116500 / YEARLY (est.)
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$98000K
$135000K

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What You Should Know About Data Engineer, Experimentation , Lyft

At Lyft, we believe in the power of community and connection, and as a Data Engineer specializing in Experimentation, you will play a crucial role in making that happen! Located in Toronto, Canada, you’ll be at the forefront of developing and enhancing our data infrastructure, ensuring we deliver seamless transportation experiences. Your mission will be to architect, design, and implement robust data pipelines that not only support our core business metrics but also empower our leadership to make informed decisions based on data-driven insights. Collaborating with a dynamic team of software engineers, data scientists, and machine learning experts, you'll be responsible for scaling up our data processing capabilities to accommodate rapid growth while building systems that uphold data quality and consistency. This is an exciting opportunity to work with technologies like Spark, Airflow, and Python, and to suggest innovative solutions that enhance our self-service data management tools. Moreover, you'll be contributing to initiatives that delight millions of riders and drivers, helping to shape the future of transportation at Lyft. If you're passionate about data, eager to explore new ideas, and ready to tackle challenges head-on, we want to hear from you! Join us today and be part of a team that's not only committed to technology but also to fostering an inclusive and diverse workplace where everyone can thrive.

Frequently Asked Questions (FAQs) for Data Engineer, Experimentation Role at Lyft
What does a Data Engineer in Experimentation at Lyft do?

As a Data Engineer in Experimentation at Lyft, you’ll be integral in developing the data infrastructure that powers key business metrics and operational insights. Your primary responsibility will be to architect and implement scalable data pipelines, ensuring they meet the needs of various teams across the organization, including Analytics and Data Science.

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What qualifications are needed for the Data Engineer role at Lyft?

To apply for the Data Engineer position at Lyft, you should have a Bachelor’s degree in Computer Science, Statistics, or a related field, along with at least 3 years of professional experience in data engineering. Strong skills in Spark, SQL, and Python are essential, along with experience in building complex data models and pipelines.

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What technologies will I work with as a Data Engineer at Lyft?

In the Data Engineer role at Lyft, you'll work with a variety of technologies, including Apache Spark, Airflow for orchestration, Trino, and PostgreSQL. Proficiency in Python for ETL processes is also key. You'll be part of a tech-savvy team that embraces modern data engineering practices.

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What is the working environment like for a Data Engineer at Lyft in Toronto?

The working environment for a Data Engineer at Lyft in Toronto is collaborative and dynamic. You'll work in a hybrid setup, spending three days a week in the office and having the flexibility to work remotely for up to four weeks a year. This allows for a balance of teamwork and personal productivity.

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How does Lyft promote diversity in its hiring practices for Data Engineers?

Lyft is committed to diversity and inclusion in its hiring practices for the Data Engineer role and beyond. They aim to provide equal opportunities without discrimination and actively seek to create an environment that welcomes diverse backgrounds and perspectives.

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What growth opportunities exist for Data Engineers at Lyft?

Data Engineers at Lyft benefit from robust growth opportunities, including the chance to work on cross-functional teams and projects that span various aspects of the business. Additionally, Lyft encourages ongoing learning and development, offering resources to help employees advance their careers.

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What are the typical responsibilities of a Data Engineer on the Experimentation team at Lyft?

Typical responsibilities for a Data Engineer on the Experimentation team at Lyft include architecting data pipelines, implementing data quality tracking systems, managing ETL processes, and participating in code reviews. You'll also support internal partners by communicating project progress and gathering requirements.

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Common Interview Questions for Data Engineer, Experimentation
Can you describe your experience with data pipeline development?

When answering this question, highlight specific projects where you built or enhanced data pipelines, including the technologies used and the challenges faced. Discuss the impact of your work on data processing efficiency or decision-making.

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How do you ensure data quality and consistency in your work?

Answer this by mentioning specific strategies, such as implementing automated tests, monitoring data integrity, and collaborating with teams to establish data management best practices. Use examples from past experiences to illustrate your methods.

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What is your approach to scaling data infrastructure?

Discuss your understanding of infrastructure requirements and experiences, including decisions around batch vs. streaming processing. Share examples of how you successfully scaled a system to accommodate growing data needs.

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How do you handle on-call responsibilities and unforeseen issues?

Explain your experience with on-call rotations and how you prioritize incident responses. Talk about your approach to troubleshooting and your strategies for communicating effectively with stakeholders during critical situations.

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Describe a significant challenge you faced as a Data Engineer and how you overcame it.

Select an example that showcases your problem-solving skills. Describe the challenge, your analysis process, the solution you implemented, and the outcome or lessons learned. This demonstrates your resilience and capacity for critical thinking.

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How proficient are you in using Spark and SQL for data processing?

Provide details about your practical experience with both SQL and Spark, including examples of projects where you utilized these technologies. Discuss any performance tuning or optimization techniques you’ve applied to improve data processing times.

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What tools do you use for workflow management, and why?

Mention specific tools you’ve worked with (e.g., Airflow) and discuss what you find beneficial about them. Highlight your familiarity with their features, advantages in a data engineering context, and how they integrate with your workflow.

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How do you communicate complex technical concepts to non-technical stakeholders?

Emphasize the importance of clear communication and provide examples of how you've successfully bridged the gap between technical and non-technical teams. Discuss your approach to simplifying jargon and using visual aids when necessary.

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Explain a time when you proposed a new data solution that was implemented.

Detail your thought process for identifying the opportunity, the steps taken to propose the solution, and the results achieved after implementation. This shows initiative and your capacity to contribute to the company’s data strategies.

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What role does collaboration play in your work as a Data Engineer?

Share your experiences working in cross-functional teams, emphasizing the importance of collaboration in data engineering projects. Discuss how you'll engage with other departments to ensure that data solutions align with business needs.

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Lyft is one of the leading ride-sharing companies in America offering services in ride-hailing, vehicles for hire, motorized scooters, a bicycle-sharing system, rental cars, and food delivery in the United States and select cities in Canada.

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CULTURE VALUES
Inclusive & Diverse
Rise from Within
Mission Driven
Diversity of Opinions
Work/Life Harmony
Customer-Centric
Social Impact Driven
Rapid Growth
BENEFITS & PERKS
Maternity Leave
Paternity Leave
Flex-Friendly
Medical Insurance
Dental Insurance
Vision Insurance
Mental Health Resources
Life insurance
Disability Insurance
Health Savings Account (HSA)
Flexible Spending Account (FSA)
401K Matching
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Full-time, hybrid
DATE POSTED
November 24, 2024

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