Senior Data Engineer - Data​/Backend

Sound Cloud empowers artists and fans to connect and share through music. Founded in 2007, Sound Cloud is an artist-first platform empowering artists to build and grow their careers by providing them with the most progressive tools, services, and resources. With over 400+ million tracks from 40 million artists, the future of music is Sound Cloud.

We’re looking for a Senior Data Engineer to join our Data Platform team, which is responsible for the efficient, reliable, secure, and compliant propagation of all datasets across the company to our data warehouses. At Sound Cloud, we rely heavily upon data-driven, informed decision making, and the Data Platform team is key to enabling that.

The Data Platform team cooperates mostly with infrastructure engineering on the back-end side, taking care of all the integrations, and builds software to make the data flow from the source into the data warehouse or the system which transforms data in real time. As a Data Engineer at Sound Cloud, you will be experienced in designing and implementing highly scalable, distributed, real-time data pipelines and developing highly scalable and secure data services in languages like Go and Scala and Java.

We also use Google Cloud Platform (Data Flow, Big Table, AI Platform, Big Query, Kafka, Kubernetes, Spark, Airflow, Terraform).

• The ideal candidate will have 3+ years of experience in back-end engineering including 1-2 years of experience setting up data processing pipelines, ideally in the cloud environment (GCP)
• Experience designing and implementing (real-time) data processing systems, facilitating technical decisions, considering technical tradeoffs
• You are a strong individual contributor driven by solving challenging technical problems
• You are comfortable participating in architecture discussions, planning new and improving existing solutions
• You are comfortable with large scale data infrastructure (PBs scale). You understand the tradeoffs that come with it

• Experience with Cloud Migrations
• Experience deploying ML models in production
• Experience with Dev Ops tools and technologies