Data Engineer I, ITC
Nike Karnataka, India
Job Description
"Embark on a rewarding career as a Data Engineer I at Nike's ITC, where you'll harness the power of technology to revolutionize the world of sports and fashion."
As a Data Engineer I at Nike's ITC, you'll be part of a dynamic team that works on cutting-edge software projects, driving innovation and business growth. Your expertise in data engineering will play a pivotal role in shaping the future of Nike's internal product tools and Consumer Product and Innovation (CP&I) community.
With a strong passion for problem-solving and collaboration, you'll work closely with engineers, Technical Product Managers, and Principal Engineers to design, develop, and deploy scalable data solutions that meet the evolving needs of Nike's global business.
Why you should learn this:
The demand for skilled Data Engineers is on the rise, with a global shortage of experts in this field. According to Glassdoor, the average salary for a Data Engineer in the United States is around $118,000 per year.
Expected Salary: $100,000 - $140,000 per year, depending on location and experience.
How it works:
- Design and develop scalable data pipelines using Apache Beam, Apache Spark, or AWS Glue to process and transform large datasets.
- Collaborate with cross-functional teams to identify data requirements, define data models, and implement data governance policies.
Core Concepts to Master
Data Engineering Fundamentals
Understand the principles of data engineering, including data warehousing, ETL (Extract, Transform, Load) processes, and data governance. Learn about popular data engineering tools and technologies, such as Apache Beam, Apache Spark, and AWS Glue.
Cloud Computing and Data Storage
Explore cloud computing platforms, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP), and learn about data storage solutions, including relational databases, NoSQL databases, and data lakes.
Data Pipelines and Streaming
Learn about data pipelines and streaming technologies, including Apache Kafka, Apache Flink, and AWS Kinesis. Understand how to design and implement real-time data processing architectures.
Data Governance and Security
Study data governance principles and best practices, including data quality, data privacy, and data security. Learn about compliance regulations, such as GDPR and HIPAA, and understand how to implement data access controls and auditing mechanisms.
Interview Questions (Beginner)
- What is data engineering, and how does it differ from data science?
- Can you explain the concept of data warehousing and ETL processes?
- How do you design and implement a data pipeline using Apache Beam or Apache Spark?
Job Overview
Advance Questions
- • How do you optimize data processing performance in a cloud-based environment?
- • Can you describe a scenario where you implemented data governance policies and data access controls?
- • How do you handle data quality issues and data anomalies in a large-scale data processing architecture?