Job Description

What You’ll Do:

• Work on our massive semi-structured datasets.
• You’ll develop software to process, transform and analyze the data to identify signals from billions of events we collect everyday.
• You’ll provide insights that improve the experience of hundreds of millions of users, and 100,000+ stores in our marketplace.

Qualifications

• 5+ years of professional experience

• 3+ years experience working in data engineering, business intelligence, or a similar role

• Bachelor’s degree in Computer Science, Software Engineering, Statistics or other quantitative fields required

• Experience with object-oriented/object function scripting languages: Python, Java, Scala, etc is a must

• Expert in database fundamentals, SQL and distributed computing

• 1+ years of experience with the Distributed data/similar ecosystem (Spark, Hive, Presto) and streaming technologies such as kafka/Flink

• Experience in setting up and managing data infrastructure

• Experience with data pipeline and workflow management tools: Luigi, Airflow, etc

• Experience with AWS cloud services

• Experience building or working with A/B testing and experiment frameworks a plus

• Communication skills, especially for explaining technical concepts to nontechnical business leaders

• Ability to work on a dynamic, research-oriented team that has multiple concurrent projects

• Proven track record of leading and mentoring in a multi-functional collaborative team environment including varied business units, vendors, countries and cultures

Here at Wish, you are joining a team and company at a time of growth and transformation. You will love being surrounded by people who are as passionate as you are about e-commerce, technology, and a data-driven culture. Even if you don’t meet 100% of the above, we encourage you to still apply!

The base salary range for this position is [$168,000 - $240,000] annually. Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, qualifications and other business considerations.

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