Dell AI Signals Recommender Data Engineer 

India, Karnataka, Bengaluru 

207070909

What you’ll achieve

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 Machine Learning/Artificial Intelligence 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.

You Will

• Design specifications for high availability and highly scalable applications which will be used by both internal and external customers

• 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 Machine Learning/Artificial Intelligence 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.

Essential Requirements

• 5+ years in primary skillset of building complex ETL orchestrating pipelines preferably using Airflow

• Data Engineerswith experience in Airflow, Python, Bash, SQL

• Education (Bachelor’s degree or higher) in Computer Science, Mathematics, or a related technical field, or equivalent practical experience

• You have professional experience. Solid experience in Data or BI engineering dealing with large complex data scenarios

• Experience with data processing software (such as Hadoop, Spark, Pig, Hive), along with data processing algorithms (MapReduce, Flume) is a plus.

Desirable Requirements

• Bachelor’s degree or equivalent and5+ years of validated experience in testing/coding/IT software

• Experience with building and designing enterprise data pipelines with various levels of priority, concurrency, and versioningand ability to work with varied data infrastructures – including relational databases, column stores, NoSQL databases, and file-based storage solutions. Exposure to machine learning or machine learning pipelines is a plus. Ability to set up containerized services using Kubernetes and Docker.