Data Engineer I, ITC
Nike Karnataka, India
Job Description
"Join Nike's Global Technology organization as a Data Engineer I and help drive innovation in product tools, collaborating with a talented team to deliver cutting-edge solutions."
As a Data Engineer I, you will be part of a dynamic team that supports Nike's Consumer Product and Innovation (CP&I) community by designing, building, and maintaining scalable data pipelines and architectures.
You will work closely with engineers, technical product managers, and principal engineers to solve complex software engineering problems and drive technical goals across the organization.
Why you should learn this:
The demand for skilled data engineers is high in the tech industry, with a projected growth rate of 14% by 2028.
Expected Salary: $118,000 - $170,000 per year, depending on location and experience.
How it works:
- Design and develop data pipelines and architectures using languages such as Python, Java, or Scala.
- Collaborate with cross-functional teams to gather requirements and implement solutions that meet business objectives.
Core Concepts to Master
Data Ingestion and Processing
Understand how to design and implement data ingestion and processing pipelines using tools such as Apache Beam, Apache Spark, or AWS Glue.
Data Storage and Management
Learn about various data storage options, including relational databases, NoSQL databases, and data warehouses, and understand how to design and implement data management systems.
Data Analytics and Visualization
Understand how to design and implement data analytics and visualization solutions using tools such as Tableau, Power BI, or D3.js.
Interview Questions (Beginner)
- What are the key differences between batch and real-time data processing?
- How do you handle data quality issues in a data pipeline?
- What are some common data storage options, and when would you use each?
Job Overview
Advance Questions
- • Design a data pipeline to process and store customer transaction data, including data aggregation and visualization.
- • Develop a data warehousing solution using a cloud-based service such as AWS Redshift or Google BigQuery.
- • Implement a real-time data processing system using a streaming platform such as Apache Kafka or AWS Kinesis.