Data Engineer with AI and Visualization Expertise

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.

About the Role

Nielsen offers the world's most comprehensive measurement of media behaviour. Our data engineers leverage the best data assets and cutting-edge AI tools to build and maintain scalable data pipelines for analysis of the complex media landscape. We help the world's biggest brands make faster, data-driven decisions about their advertising and marketing strategies. As a Data Engineer with AI and visualization expertise, you will be responsible for designing, building, and maintaining high-performance data pipelines, leveraging your deep understanding of AI prompting techniques and creating compelling visualizations in Tableau, bringing it all together to deliver insightful analyses to stakeholders,.

This role is part of the Advanced Analytics team responsible for supporting data needs across the Commercial Product Sales unit. Requires deep knowledge of Python, SQL, and cloud platforms (AWS or GCP) to build and manage data pipelines. The ideal candidate will be someone with a strong understanding of AI prompting techniques for driving efficient and accurate results is crucial. Having expertise into data visualization with the ability to translate complex large dataset into clear, concise actionable insights is desired. Creativity, problem-solving skills, and a collaborative spirit are essential for this role. You will need to be a critical thinker, able to identify roadblocks and proactively propose solutions. A passion for data exploration, AI, and a commitment to continuous learning are key.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Python, R, SQL, and cloud platforms (e.g., AWS or GCP).

• Build and optimize ETL processes to efficiently extract, transform, and load data for analysis.

• Conduct advanced statistical analysis and leverage AI techniques to provide actionable insights, identify trends, and measure performance.

• Ability to code in SQL, Python, Spark etc. to be able to extract data from various data tables on the cloud and create workflows/pipelines for various custom solutions

• Stay abreast of emerging data engineering, visualization, and AI tools and techniques, continuously seeking improvements and optimizing existing processes.

• Support ad hoc data requests, ensuring timely delivery of accurate information.

• Utilize Tableau to build interactive and comprehensive always-on dashboards and visualizations to prompt actionable insights.

• Holds an advanced degree in a technical field such as Computer Science, Data Engineering, Mathematics, Statistics, or a related area.

• Proven 3+ years of experience in data engineering roles, with strong expertise in Python, SQL, Google Scripts and PySpark.

• Familiarity with data warehousing concepts, including data modelling and ETL processes.

• Understanding of cloud platforms (e.g., AWS or GCP) for data processing and analysis.

• Deep understanding of AI prompting techniques, including experience with different AI models and tools.

• Strong analytical skills, adept at problem-solving and identifying trends in complex datasets with an ability to interpret data into actionable insights.

• Extensive knowledge of Tableau, with a demonstrated ability to create impactful dashboards and visualizations.

• Excellent communication skills, both written and verbal, with the ability to translate technical information into layman's terms.

• Ability to work independently and collaboratively within a team environment.

• Ability to merge and transform diverse internal and external datasets to create innovative features and new custom product solutions

• Demonstrates strategic thinking, understanding the broader impact of data insights on product and business objectives.

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