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The Winding Road to Better Machine Learning . . . - Spotify Engineering We decided to meet our teams where they were by building connectors amongst the most popular ML frameworks in active use instead of dictating to teams which framework to use At the time, our data tooling used the Scala language heavily – especially Scio (previous blog post), our open-source Data Processing library built on top of Apache Beam
GitHub - GopikaJL -Spotify-Data-Engineering-Framework This repository demonstrates a comprehensive data engineering and analysis pipeline leveraging Spotify’s Web API and Apache Spark The project focuses on extracting, processing, and analyzing data from Spotify’s Global Top 50 playlist By employing a multi-layered data architecture (bronze
Machine Learning Engineer - Content Understanding | Life at Spotify You have some hands-on experience implementing or prototyping machine learning systems at scale You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark
Why We Switched Our Data Orchestration Service - Spotify Engineering TL;DR Within Spotify, we run 20,000 batch data pipelines defined in 1,000+ repositories, owned by 300+ teams — daily The majority of our pipelines rely on two tools: (for the Python folks) and (for the Java folks) In 2019, the data orchestration team at Spotify decided to move away from these tools In this post, the team details why the decision was made, and the journey they took to make
Data Engineer - Personalization at Spotify - startup. jobs You have proven experience in data engineering, including creating reliable, efficient, and scalable data pipelines using data processing frameworks such as Scio, DataFlow, Beam or equivalent You are comfortable working with large datasets using SQL and data analytics platforms such as BigQuery
Kousikdutta1 Spotify-Data-Pipeline-Using-Airflow - GitHub Spotify-Data-Pipeline-Using-Airflow Developing an intricate data engineering pipeline tailored for Spotify, this project leverages the power of Airflow to seamlessly orchestrate the extraction, transformation, and storage of Spotify data, enabling in-depth analysis and insights
Senior Machine Learning Engineer - Life at Spotify Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications Hands-on expertise with implementing end-to-end production ML systems at scale in Java, Scala, Python, or similar languages
Data Engineering, Content Understanding - Spotify | Built In As a Software Engineer in the Content Understanding teams, you will build large-scale batched and real-time data pipelines using various tools and frameworks Collaborating with other engineers and data scientists, you will ensure high-quality data solutions and work on machine learning projects to enhance user experiences
Data Platform Explained Part II - Spotify Engineering Data Management and Data Processing are essential for Spotify to effectively manage its extensive data and pipelines It’s crucial to maintain data traceability (lineage), searchability (metadata), and accessibility, while implementing access controls and retention policies to manage storage costs and comply with regulations
Unified Pipeline Architecture: The Evolution of Data Processing at Spotify Erin Palmer is an applied data scientist at Spotify and software engineer with over 6 years industry experience and a Masters degree in Machine Learning and Computer Vision Previously, she worked at Google on the AdWords platform making quality improvements in the automatic bidding space, as well as at FactSet, where she worked primarily on the backend of the Portfolio Analytics application