Position: Python PySpark Engineer
Company: GlobalLogic
Category: Python
Experience required: 5y
Published: 2021-11-01T00:00:00+02:00

Job Description
Strong analytical skills who can analyse the data inconsistencies and derive meaningful insights from the large data.
Good knowledge on Hadoop Architecture and its ecosystem.
Experience in optimizing Hive SQL queries and Spark Jobs.
Problem-solving skills including knowledge of data structure and algorithms.
Experience on agile software development process.
Spark experience using python on Dataproc/Cloudera is desired.
Like to collaborate within engineering and beyond.


Job Responsibilities
Work with the product team on requirements analysis and make technical trade off decisions at application level (e.g. component design).
Define and develop solutions to technical problems that arise.
Use your expertise to input into reengineering and design.
Act as a subject matter expert for focus areas across the technology space.
Follow and improve the coding standards with the team, ensure the high code quality.
Actively look for the latest tools and technologies within or outside dunnhumby.
Actively learn and help implementing the new industry standards related to products, people, and processes.
Identify the inefficiencies in the process and help the team to improve them to maximize the team’s performance.
Help identifying the dependencies, risks, and bottlenecks in the projects proactively. Work actively with the Engineering Manager/Team Lead to resolve them.


Department/Project Description
The client is a global leader in Customer Data Science, empowering businesses everywhere to compete and thrive in the modern data-driven economy.

Product help find new value in their existing assets by monetizing them effectively – from in-store signage to websites and apps. A world-leading data science capabilities improve the effectiveness of Retail marketing, giving Brands the confidence to invest their ad spend, and driving up the value of media inventory over the long term.