Amazon Music is awash in data! To help make sense of it all, the DISCO (Data, Insights, Science & Optimization) team: (i) enables the Consumer Product Tech org make data driven decisions that improve the customer retention, engagement and experience on Amazon Music. We build and maintain automated self-service data solutions, data science models and deep dive difficult questions that provide actionable insights. We also enable measurement, personalization and experimentation by operating key data programs ranging from attribution pipelines, northstar weblabs metrics to causal frameworks. (ii) delivering exceptional Analytics & Science infrastructure for DISCO teams, fostering a data-driven approach to insights and decision making. As platform builders, we are committed to constructing flexible, reliable, and scalable solutions to empower our customers. (iii) accelerates and facilitates content analytics and provides independence to generate valuable insights in a fast, agile, and accurate way. This domain provides analytical support for the below topics within Amazon Music: Programming / Label Relations / PR / Stations / Livesports / Originals / Case & CAM. DISCO team enables repeatable, easy, in depth analysis of music customer behaviors. We reduce the cost in time and effort of analysis, data set building, model building, and user segmentation. Our goal is to empower all teams at Amazon Music to make data driven decisions and effectively measure their results by providing high quality, high availability data, and democratized data access through self-service tools.
If you love the challenges that come with big data then this role is for you. We collect billions of events a day, manage petabyte scale data on Redshift and S3, and develop data pipelines using Spark/Scala EMR, SQL based ETL, Airflow and Java services.
We are looking for talented, enthusiastic, and detail-oriented Business Intelligence Engineer, who knows how to take on big data challenges in an agile way. Duties include big data design and analysis, data modeling, and development, deployment, and operations of big data pipelines. You'll help build Amazon Music's most important data pipelines and data sets, and expand self-service data knowledge and capabilities through an Amazon Music data university.
DISCO team develops data specifically for a set of key business domains like personalization and marketing and provides and protects a robust self-service core data experience for all internal customers. We deal in AWS technologies like Redshift, S3, EMR, EC2, DynamoDB, Kinesis Firehose, and Lambda. Your team will manage the data exchange store (Data Lake) and EMR/Spark processing layer using Airflow as orchestrator. You'll build our data university and partner with Product, Marketing, BI, and ML teams to build new behavioural events, pipelines, datasets, models, and reporting to support their initiatives. You'll also continue to develop big data pipelines.
Key job responsibilities
• Design, development and ongoing operations of scalable, performant data warehouse (Redshift) tables, data pipelines, reports, dashboards and data transformation strategies to manage a large volume of data.
• Development of moderately to highly complex data processing jobs using appropriate technologies (e.g. SQL, Python, Spark, AWS Lambda, etc.)
• Development of dashboards and reports.
• Collaborate with internal stakeholders to understand business requirements and provide data driven solutions.
• Develop complex SQL queries and optimize performance of SQL queries on large data.
• Build and manage dashboards, scorecards, and other data visualization using BI tools such as Tableau, Power BI, or AWS Quicksight.
• Monitor Tableau and QS dashboards to ensure latest data is available, address any technical issues in dashboard refresh (fixing failed jobs, re-running jobs/pipelines, tracking upstream dependencies), coordinate to resolve technical issues, inform Stakeholders in case of any unavoidable delays, enhancing existing dashboards based on low-level specifications.
• Conduct thorough data analysis and troubleshoot data integrity issues, providing gap analysis and business solutions.
• Actively manage the timeline and deliverables of projects, anticipate risks and resolve issues.
• Adopt Business Intelligence best practices in reporting and analysis.
• Monitor WBR jobs, address any issues (fixing failed jobs, re-running jobs/scripts, tracking upstream dependencies) co-ordinate for any technical issues, and gathering inputs to update commentary for the metric fluctuations.
• Repeating an existing analysis for new scenarios (geographies, tier, device types) based on existing analyses, pulling datasets using low-level specifications
• Monitoring Datanet & Cradle based pipelines and data pipelines that feed metrics into APT (Weblab Analysis Tool) to address failures & delays, coordinate for resolution of technical issues, creating & monitoring alarms/checks on the pipelines for tracking delays & ensuring data quality, creating pipelines (or updating existing pipelines) based on low-level specifications and running & monitoring back-fill jobs to generate historical datasets based on already existing pipeline jobs, Coordinating with APT team to onboard (or update) APT metrics.
About the team
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators.From personalized music playlists to exclusive podcasts,concert livestreams to artist merch,we are innovating at some of the most exciting intersections of music and culture.We offer experiences that serve all listeners with our different tiers of service:Prime members get access to all music in shuffle mode,and top ad-free podcasts,included with their membership;customers can upgrade to Music Unlimited for unlimited on-demand access to 100 million songs including millions in HD,Ultra HD,spatial audio and anyone can listen for free by downloading Amazon Music app or via Alexa-enabled devices.Join us for opportunity to influence how Amazon Music engages fans, artists,and creators on a global scale.
- 2+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with scripting language (e.g., Python, Java, or R)
- Knowledge of data modeling and data pipeline design
- Experience gathering business requirements, using industry standard business intelligence tool(s) to extract data, formulate metrics and build reports
- Bachelor's degree in BI, finance, engineering, statistics, computer science, mathematics, finance or equivalent quantitative field
- Knowledge of AWS products such as Redshift, Quicksight, and Lambda.
- Excellent verbal/written communication & data presentation skills, including ability to succinctly summarize key findings and effectively communicate with both business and technical teams.
- Experience in the data/BI space
- Experience with building and maintain basic data artifacts (e.g. ETL, data models, queries)
- Experience with data-specific programming languages/packages such as R or Python Pandas.
- Experience with AWS solutions such as EC2, DynamoDB, S3, and EMR.
- Knowledge of machine learning techniques and concepts.