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SeniorГибридПолная занятость

Senior Data Engineer, PickupXP

Оценка ИИ

Отличная позиция в топовой технологической компании с прозрачной вилкой оплаты и интересными задачами на стыке Big Data и ML. Гибридный формат работы в Торонто и сильная инженерная культура делают это предложение очень привлекательным.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует глубоких знаний в области распределенной обработки данных (Spark, Trino) и опыта работы с высоконагруженными системами. Высокая планка ожиданий от Senior-специалиста включает не только разработку, но и архитектурное планирование, а также участие в дежурствах (on-call).

Анализ зарплаты

Медиана155 000 CA$
Рынок130 000 CA$ – 185 000 CA$
Оценка ИИ

Предложенная зарплата (136k - 170k CAD) полностью соответствует рыночным ожиданиям для Senior Data Engineer в Торонто. Верхняя граница диапазона даже несколько превышает медиану, что характерно для крупных технологических компаний уровня Lyft.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Data Engineer position within the PickupXP team at Lyft. With over five years of experience in building robust data pipelines and a deep expertise in Spark, Airflow, and SQL, I have consistently delivered scalable solutions that handle massive datasets. My background in optimizing ETL processes and collaborating with cross-functional teams aligns perfectly with Lyft's mission to enhance the rider and driver experience through seamless mapping services.

In my previous roles, I have focused on developing maintainable code and evolving data models to meet rapidly growing business needs. I am particularly drawn to this opportunity because of the team's focus on high-impact projects like shipping ML models at scale and improving real-time routing signals. I am confident that my technical skills in Python and Trino, combined with my passion for performance engineering, will allow me to contribute significantly to the Mapping organization at Lyft.

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Откликнитесь в lyft уже сейчас

Присоединяйтесь к команде Lyft в Торонто и создавайте будущее картографических сервисов для миллионов пользователей!

Описание вакансии

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.

 Our mission depends on having a digital representation of the physical world - a map with all routing related (real-time) information. This is what makes Lyft different from many products: our products don’t just facilitate online interactions, they facilitate dynamic, real-world ones. Without mapping services, none of these real world interactions between people and transport can happen.

We are hiring a Senior Data Engineer to join our Pickup, Places and Search team within the Mapping organization that is responsible for building, enhancing and maintaining the Rider and Driver product for the Pickup/Drop off Experience. This team supports the backbone of Lyft’s search system, supporting millions of rides by helping our riders and drivers connect and reach their destination. Our focus is to ensure the pickup/dropoff experience is seamless by enhancing guidance and routing as well as the rider experience. Additionally the team uses map data and other signals to create new product experiences in collaboration with teams across Lyft. This team has a history of enabling rich and creative features that directly influence the product for all of our users. We constantly innovate and incorporate cutting-edge technologies to make the lives of our community more enriched.

In this role, you'll collaborate with other engineering teams, product, data science, analytics, and operations on programs that empower us to iterate quickly, delighting our passengers and drivers with rideshare focused mapping experiences.

Responsibilities:

  • Owner of core company data pipelines, responsible for scaling up data processing flow to meet the rapid data growth at Lyft
  • Continuously evolve data models and schemas based on business and engineering requirements
  • Implement and maintain systems to monitor and enhance data quality and consistency
  • Develop tools that support self-service management of data pipelines (ETL) and optimize data processing performance
  • Write well-crafted, well-tested, readable, maintainable code that prioritizes scalability and cost efficiency
  • Conduct code reviews to ensure code quality standards and facilitate knowledge sharing
  • Participate in on-call rotations to maintain high availability and reliability of workflows and data pipelines
  • Collaborate with internal and external partners to remove blockers, provide support, and achieve results
  • Help establish technical roadmaps and architectures based on technology and our business needs.
  • Drive high-impact projects and innovate new solutions to provide the best mapping experience possible.
  • Ship ML models at scale and low cost by focussing on systems / performance engineering.

Experience:

  • 5+ years of relevant professional experience in data engineering or a related field
  • Strong expertise in SQL and experience with Trino or Spark/PySpark
  • Experience building and optimizing complex data models and pipelines with a strong understanding of ETL processes
  • Expertise in data modeling and ETL, with experience developing and optimizing complex data models and pipelines
  • Hands-on experience with workflow management tools (e.g., Airflow or similar)
  • Proficiency in a scripting language like Python or Bash
  • Comfortable working directly with cross-functional teams (data analytics, data science, engineering) to align solutions with business goals

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 CAD $136,000 - CAD $170,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

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Навыки

  • SQL
  • Trino
  • Spark
  • PySpark
  • ETL
  • Airflow
  • Python
  • Bash
  • Data Modeling
  • Machine Learning

Возможные вопросы на собеседовании

Учитывая масштаб Lyft, важно понимать, как кандидат справляется с ростом объема данных.

Расскажите о самом сложном случае оптимизации производительности Spark-джоба, с которым вы сталкивались. Какие метрики вы использовали для диагностики?

Вакансия упоминает работу с ML-моделями на уровне системной инженерии.

Каков ваш опыт в развертывании и поддержке инфраструктуры для ML-моделей в продакшене? Как вы обеспечиваете низкую задержку при обработке признаков?

Для картографических сервисов точность данных критична.

Какие стратегии и инструменты вы используете для обеспечения качества данных (Data Quality) и консистентности в распределенных системах?

Роль предполагает владение Airflow или аналогичными инструментами.

Как бы вы спроектировали отказоустойчивый ETL-процесс, который должен обрабатывать данные в реальном времени с минимальным лагом?

Вакансия Senior-уровня подразумевает лидерство и менторство.

Опишите ситуацию, когда вам пришлось убеждать кросс-функциональную команду (например, Data Science или Product) принять определенное техническое решение. Как вы аргументировали свою позицию?

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lyft
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