Lead Data Engineer Jobs
By Capital One At Richmond, VA, United States
At least 6 years of experience in application development (Internship experience does not apply)
At least 2 years of experience in big data technologies
At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud)
7+ years of experience in application development including Python, SQL, or Scala
4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud)
4+ years experience with Distributed data/computing tools (EMR, Kafka, or Spark)

Are you an experienced Data Engineer looking to take your career to the next level? We are looking for a Senior Lead Data Engineer to join our team and help us build the future of data-driven products. You will be responsible for developing and managing data pipelines, designing and implementing data architectures, and leading the development of data-driven solutions. If you have a passion for data and a drive to make a difference, this is the perfect opportunity for you!

A Senior Lead Data Engineer is responsible for designing, developing, and maintaining data systems and pipelines. They must have strong technical skills and be able to work with a variety of data sources and technologies.

What is Senior Lead Data Engineer Skills Required?

-Strong knowledge of data engineering principles and best practices -Experience with data warehousing, ETL, and data modeling -Proficiency in SQL and NoSQL databases -Experience with big data technologies such as Hadoop, Spark, and Kafka -Familiarity with cloud computing platforms such as AWS and Azure -Knowledge of scripting languages such as Python and R -Ability to work independently and collaboratively

What is Senior Lead Data Engineer Qualifications?

-Bachelor’s degree in Computer Science, Information Systems, or related field -5+ years of experience in data engineering -Strong problem-solving and analytical skills

What is Senior Lead Data Engineer Knowledge?

-Knowledge of data engineering principles and best practices -Knowledge of data warehousing, ETL, and data modeling -Knowledge of SQL and NoSQL databases -Knowledge of big data technologies such as Hadoop, Spark, and Kafka -Knowledge of cloud computing platforms such as AWS and Azure -Knowledge of scripting languages