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About Us Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on and , and discover the projects we’re solving on our . Be sure to explore our to learn how to ace our interview process. About the Role Community Discovery ML team focus on providing personalized, relevant experience for Twitch users through Recommendation and Search. We are looking for a senior data engineer to join us. You will be the first data engineer hired in a hybrid team of ML engineers and scientists and working on data challenges related to ML models and products. You will extend, design and build new capabilities in our data systems to ensure fast ML model development and productionization. You will impact cross teams by defining expectations for data usage patterns and data quality. You will report to an Engineering Manager and work in San Francisco / Bay Area. You Will: Oversee team data architecture to meet ML use cases in production. Design and build scalable data pipelines to support personalization models. Develop and maintain low-latency, large-scale streaming and batch data processing systems. Collaborate with applied scientists and ML engineers to integrate data into production models. Optimize data workflows for performance and cost efficiency. Implement best practices for data governance and security. Troubleshoot and resolve data-related issues, with a focus on identifying and solving data quality problems. Mentor others in the team in data related solutions and skills. You Have: 6+ years of experience as a data engineer or in a similar role. Proficiency in SQL, Python, or Scala. Experience with building batch and streaming data pipelines with high throughput and low latency. Strong understanding of data architecture and data modeling principles. Experience analyzing large datasets to identify gaps and inconsistencies, provide data insights, and promote effective product solutions Hands-on experience with cloud platforms (AWS, GCP, or Azure) and their data services. Familiarity with ETL tools and data warehousing solutions. Experience with distributed data processing technologies such as Apache Spark, Flink, and Kafka. Experience working with cross-functional roles like ML engineers and scientists. Bonus Points Experience with AWS data ecosystems like Redshift, Kinesis and Glue. Understand data requirements for ML production systems. Extensive experience with mature and large-scale production data systems and capable of defining a strong North Star and making increments progress towards that. Perks Medical, Dental, Vision & Disability Insurance 401(k) Maternity & Parental Leave Flexible PTO Amazon Employee Discount
