Job Description :
Job Details:
Data Engineer
Location: Bradenton, FL
Duration: 12+ months

Summary of Position:
Client is seeking an innovative individual who has a proven track record of building enterprise level platform components to support product development from multiple teams and lines of business. This role is expected to drive innovation through collaboration across our data science teams and business to help push Foot Locker, Inc. to the next level. The team is embarking on a journey of building a brand-new data lake platform built using cloud native concepts and the latest tech stacks. Some ideal technologies for this individual would be Spark, Scala/Python, Kafka, and Azure (AWS is ok too

Create Apache Spark code and load it into tables
Build new data sets, and products helping support Foot Locker business initiatives
Must be able to contribute to self-organizing teams with minimal supervision working within the Agile / Scrum project methodology
Build production quality ingestion pipelines with automated quality checks to help enable the business to access all of our data sets in one place
Support our Data Scientists by helping enhance their modeling jobs to be more scalable when modeling across the entire data set

Strong ETL / Data Modeling Experience
Must have minimum 2 years of hands-on experience in Apache Spark
Bachelors Degree in Computer science or related field
3+ or more years of related information technology experience
2 years of experience in Spark, Scala, Python, Hive, and Data Lake
Experience in developing ETL Processes
Strong SQL Skills
Good understanding of data warehouse design(e.g. dimensional modeling)
Demonstrated experience with agile scrum methodology
Must possess well-developed verbal and written communication skills

Nice to Have’s:
Public cloud experience, preferably Azure or AWS
Experience using CI / CD tools and project build automation (Git, Jenkins, Maven, PyPi, etc
Previous experience at a fortune 500 company
Experience with enabling Data Science and Self-Service product development with clean, reliable data sets.