At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.
Job DescriptionABOUT THE JOB
We are looking for self-motivated, creative thinkers, people that are flexible and enjoy working in teams.
ABOUT THE TEAM
Our dedicated data engineering team assumes full responsibility for the entire lifecycle of our “data processing platform.” This transformative platform empowers our colleagues from diverse departments, even those lacking extensive technical expertise, to independently construct and manage end-to-end Airflow data processing pipelines. Utilising the scalability of Spark clusters on AWS, the platform offers an intuitive, virtually no-code interface that enables users to effortlessly assemble, transform, and serialise data. This capability is paramount for supporting our sophisticated media planning solutions, democratising data access and manipulation, and substantially expediting the delivery of valuable insights without the conventional reliance on coding proficiency.
The team has an open culture, works in an agile style and in close cooperation with software developers and colleagues from other disciplines, such as data scientists and client-facing solution managers. You will have the opportunity to develop yourself in areas like big data, cloud computing, data lake architecture and data orchestration.
Qualifications5+ years of experience in data engineering
Bachelor or master’s degree in Computer Science Data Engineering, or proven experience in the field;
Knowledge of and experience with software engineering best practices is crucial.
Proficiency in distributed computational engines such as Spark, Presto, and Trino.
Familiarity with cloud platforms like AWS, Azure, and Google Cloud Platform (GCP).
Experience with containerisation technologies (Docker, Kubernetes, etc.).
Knowledge of continuous integration pipelines.
Ability to interact with Data science teams as key stakeholders
Capability to design scalable data processing pipelines that can efficiently handle data volumes ranging from a few megabytes to terabytes, while maintaining consistency in applying the same business rules.
Fast learner
Team player with flexible, proactive and pragmatic attitude;
Excellent problem-solving and analytical skills. Strong communication and collaboration skills.
Ability to guide fellow team members
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