Job description
We are currently building a company wide Data Lake in the (AWS) Cloud. It is actively used by our Sales, Marketing & Pricing teams Data Scientists in our Pricing & Analytics Center of excellence team.
This team is implementing cutting edge technologies for optimizing HP pricing and marketing spends world-wide. Access to a variety of gold datasets in the most efficient manner is critical.
Our team has a worldwide impact, having teams located on all continents. You will be leading a team of 3 to 6 data engineers and occasionally contingent workers, who are responsible for bringing data requested by the teams of data scientists into this data lake.
With your team, you will make sure that those assets are properly built (data architecture, efficient ETL processing), documented, and evangelized to broad audience of users.
Key Responsibilities
- Organize your team in a “follow the sun” (Global workflow) organization, and leverage to have footprints in all continents
- Assigns responsibilities, provides direction, leadership, and coaching, removes barriers as needed to enable team to execute their roles and achieve objectives and goals.
- People recruitment and development
- Cost management, and direction-setting within the area of responsibility.
- Give guidance, advise and bring into our data lake any data required, constrained by our context and technology stack
- Leverage our stack in the fastest & most efficient manner: S3, Kubernetes cluster, Dataiku DSS, Spark (pySpark, Scala, Spark R), python, R, Julia languages, data partitioning
- Guide and coach customers to access and use this data appropriately
- Architecture organization of data subjects, and segment by gold/silver/platinum qualities
Requirements:
- Typically, 8+ years of relevant experience
- Bachelor's degree in Computer Science, Engineering
- Experience directly leading and developing a team
- Familiar with Agile Project Management.
- In-depth understanding of technology in direct responsibility.
- Deep understanding of Spark (experience on Spark optimization would be ideal)
- Proficiency with Python & R
- Experience working in a big data environment is required (hdfs, partitioning concept)
- SQL proficiency, experience of ETL activities
- Experience with Cloud environment (AWS preferred)
- Experience with Kubernetes, docker
Bonus points
Experience with Dataiku DSS (pySpark, Scala is a plus)
About HP
