Software Engineer - Data Backend

Description:

We're looking for entry-level engineers who are eager to learn and contribute to building elegant, scalable systems. You'll have the opportunity to work with SQL and NoSQL data stores, data warehouses, batch processing, and stream processing solutions. Join us in leveraging machine learning across Yelp to address significant business challenges, improve user experiences, facilitate data-driven decisions, and maintain the reliability of Yelp's content. If you are enthusiastic about learning, eager to take on new challenges, and passionate about enhancing systems, we want you on our team!

Yelp engineering culture is driven by our values: we’re a cooperative team that values individual authenticity and encourages creative solutions to problems. All new engineers deploy working code their first week, and we strive to broaden individual impact with support from managers, mentors, and teams. At the end of the day, we’re all about helping our users, growing as engineers, and having fun in a collaborative environment.

This opportunity is fully remote and does not require you to be located in any particular area in Canada. We welcome applicants from throughout Canada. We’d love to have you apply, even if you don’t feel you meet every single requirement in this posting. At Yelp, we’re looking for great people, not just those who simply check off all the boxes.

What you'll do:

• Contribute to building systems that can effectively store and crunch terabytes of data.

• Support the infrastructure that empowers millions of Yelp’s users to make the best decisions.

• Engage with diverse challenges such as personalizing ads and search ranking, AI telephony and chatbots, advertiser retention and churn prevention, data-driven storytelling, clickstream analytics, content type classification, delivering personalized recommended businesses to users and sophisticated bot detection.

• Collaborate with cross functional teams, including software engineers, product managers and data scientists to identify and use the most relevant consumer and business data.

• Develop expertise in cutting-edge infrastructure for machine learning or data analytics or product feature use cases.

• Learn the fine art of balancing scale, latency and availability depending on the problem.

What it takes to succeed:

• Good coding skills in Python or equivalent (ideally Java or C++).

• A passion for architecting large systems with elegant interfaces that can scale easily.

• A hunger for tracking down root causes—no matter how deep it takes you—and fixing them in systematic ways.

• Understanding of building data pipelines to train and deploy machine learning models and/or ETL pipelines for metrics and analytics or product feature use cases.

• Exposure to some of the following technologies: Apache Spark, AWS Redshift, AWS S3, Cassandra (and other NoSQL systems), AWS Athena, Apache Kafka, Apache Flink, Java, AWS and service oriented architecture.