Data Engineer
Cummins India Pune Division, Maharashtra, India
Job Description
"Unlock the power of data engineering at Cummins India and drive business growth through innovative data solutions."
In this role, you will be responsible for developing and maintaining a robust data and analytics platform that enables agile data delivery at scale. You will work closely with business and IT teams to understand their requirements and leverage cutting-edge technologies to drive business outcomes.
As a Data Engineer at Cummins India, you will have the opportunity to work on a wide range of projects, from implementing data governance processes to developing reliable and efficient data pipelines. If you are passionate about data, technology, and driving business growth, this role is perfect for you.
Why you should learn this:
The demand for skilled data engineers is on the rise, with a projected growth rate of 14% in the next five years.
Expected Salary: The average salary for a data engineer in India is between ₹1,200,000 - ₹2,500,000 per annum, depending on experience and location.
How it works:
- Step 1: Understand the business requirements and collaborate with stakeholders to design a data architecture that meets their needs.
- Step 2: Implement data ingestion and transformation pipelines using distributed systems and tools such as Apache Kafka, AWS Kinesis, and Apache Spark.
Core Concepts to Master
Data Governance
Data governance involves establishing processes and policies to manage data quality, metadata, access, retention, and security. As a data engineer, you will implement data governance processes to ensure data accuracy, consistency, and compliance with regulatory requirements.
Data Pipelines
Data pipelines involve designing and implementing efficient and scalable data processing systems to extract, transform, and load data from various sources. You will develop reliable and efficient data pipelines using tools such as Apache Beam, AWS Glue, and Apache Airflow.
Distributed Systems
Distributed systems involve designing and implementing systems that can process and store large amounts of data in a scalable and fault-tolerant manner. You will implement distributed systems using tools such as Apache Hadoop, Apache Cassandra, and Apache Spark.
Data Quality and Integrity
Data quality and integrity involve ensuring that data is accurate, complete, and consistent. You will implement methods to continuously monitor and troubleshoot data quality and data integrity issues using tools such as Apache NiFi, Apache Kafka, and Apache Spark.
Interview Questions (Beginner)
- What is data governance, and how do you implement it?
- Can you explain the difference between batch processing and real-time processing?
- What is a data pipeline, and how do you design and implement one?
Job Overview
Advance Questions
- • How do you design and implement a distributed system for ingesting and transforming data from various sources?
- • Can you explain the concept of data quality and integrity, and how do you ensure it?
- • How do you implement data governance processes and methods for managing metadata, access, retention, and security?