At Nielsen, we believe that career growth is a partnership. You ultimately own, fuel and set the journey. By joining our team of nearly 14,000 associates, you will become part of a community that will help you to succeed. We champion you because when you succeed, we do too. Embark on a new initiative, explore a fresh approach, and take license to think big, so we can all continuously improve. We enable your best to power our future.
Background
Gracenote is the world’s leading entertainment data and technology company. We power the top music services, consumer electronics companies, automakers, media companies and cable and satellite operators on the planet. At its core, Gracenote helps people find, discover and connect with the entertainment they love.
Gracenote does this by
1) Global Databases - Creating, collecting and organizing detailed information about TV shows,
sports, movies and music
.
2) Best-in-Class Experience - Building best-in-class services and technologies to make TV, sports, movies and music more accessible to and discoverable by fans.
3) Highly Scalable Platform - All on a highly scalable platform that serves billions every month. The data Gracenote creates and the tech we invent is essential to 80% of Forbes’ Most Valuable Brands in Automotive, Media and Tech.
Job Purpose
We are presently looking for a Data Engineer as a part of our Gracenote Quality Office team. The Quality Office is responsible for designing and implementing quality management solutions across Product, Operations, Technology & Commercial.
This role is to assist in the organization’s journey to leverage our vast metadata content. Collaborating with multiple teams to measure and improve data quality, and access and combine datasets for use in quality projects, is paramount to succeed in this role. Familiarity with data integration, AWS cloud services, analytical systems, data harmonization, and data automation is key in this role.
Responsibilities:Works with the Quality Office and cross-functional teams on data quality and implement systems to make them visible with metrics and thresholdsResponsible for building data pipelines and data integration projects to measure qualityBuild and optimize data queries from multiple relational databases, data warehouses, and data lakes for various analyses and requestsSupport the team to production business requests and analysis, while building tools and infrastructure to improve their speed and effectivenessAbility to translate raw data into visualizations to be understood by multiple (technical and non-technical stakeholders) or in data staging.Build on the cloud Core Requirements Data engineer with 3-5 years experience in cloud (AWS etc), Python, Spark, SQL, Big-data solutions Clear database fundamentals, data integration, transformation, distributed computing, and building robust and scalable solutions.Knowledge of API, BI and AI/ML is preferred.ABOUT NIELSEN :
As the arbiter of truth, Nielsen Global Media fuels the media industry with unbiased, reliable data about what people watch and listen to. To discover what’s true, we measure across all channels and platforms—from podcasts to streaming TV to social media. And when companies and advertisers are armed with the truth, they have a deeper understanding of their audiences and can accelerate growth.
Do you want to move the industry forward with Nielsen? Our people are the driving force. Your thoughts, ideas and expertise can propel us forward. Whether you have fresh thinking around maximizing a new technology or you see a gap in the market, we are here to listen and take action. Our team is made strong by a diversity of thoughts, experiences, skills, and backgrounds. You’ll enjoy working with smart, fun, curious colleagues, who are passionate about their work. Come be part of a team that motivates you to do your best work!
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Estimated Salary range:
Our salary intelligence, powered by our AI algorithms, has calculated an estimated range for this position between $10,000 - $20,000 . Please note that the actual salary for this position may vary, as it is dependent on various factors including but not limited to experience, location, and market dynamics.
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