Big Data Engineer Sr. Analyst

We are transforming Marketing at Dell. Come join the best and the brightest to bring “Personalization at Scale” vision to life. Leveraging Dell Technology intellectual property, we are building one of the world's largest customer data lake bringing billions customer contacts and data together to drive our #digitaltransformation. If you love changing the status quo, and empowering the curious to engineer marketing excellence - we invite the curious to join our Modeling & Customer Intelligence team. #GoBig #GoFast.

About the position

Our data scientists & engineers are helping to shape our marketing strategy for a digital world.

What are our goals in Marketing?

Drive perception;
Drive demand;
Grow the buyer base;
Build the best content.

The models and solutions you deliver will be the essential ingredient we use to supply our customers & sales with relevant product recommendations and creative breakthroughs to our business.

You will work with other engineers and data scientists to solve some of the hardest business problems. You will learn what it takes to build, deploy & scale ML/AI models in real-world (it’s a hard problem, we assure you). You will build analytical data sets on which model’s will be built. This would entail two key tasks – feature engineering on large datasets and optimization of our pipelines to drive scalability. If you are already good at it, we will make you better.

We offer you a fast-paced work environment in a diverse team with exposure to Dell teams across the globe. You will be part of a dynamic, collaborative and cross-functional team focused on making significant business impacts.

Key Responsibilities

• Feature engineering: Stitching together and aggregation of multiple large data sets working with the data scientist to a desired format
• You will need to optimize both queries and architecture to support big data sets. SQL is usually the preferred language, but some Spark jobs are necessary
• Data pipelining: Once the initial dataset are prepared, it is normally run through a further pipeline to prepare it for modelling. Here you will create features that make ML/AI algorithms work (e.g. translating text into category variables). Python/R is usually the preferred language