Data Engineer for Machine Learning Pipelines
2 weeks ago
Data Engineer for Machine Learning Pipelines
Be part of a team that ensures Dell Technologies' product integrity and customer satisfaction. Our IT Software Engineer team turns business requirements into technology solutions by designing, coding, and testing/debugging applications, as well as documenting procedures for use and constantly seeking quality improvements.
Key Responsibilities
- Design specifications for high availability and highly scalable applications that will be used by both internal and external customers.
- Work with other engineers and data scientists to solve some of the hardest business problems.
- Learn what it takes to build, deploy, and scale Machine Learning/Artificial Intelligence models in real-world scenarios.
- Build analytical data sets on which models will be built, involving feature engineering on large datasets and optimization of pipelines to drive scalability.
Requirements
- 5+ years of experience in building complex ETL orchestrating pipelines preferably using Airflow.
- Data Engineers with 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.
- 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 and 5+ years of validated experience in testing/coding/IT software.
- Experience with building and designing enterprise data pipelines with various levels of priority, concurrency, and versioning, and 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.
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