The salary of a Big Data Engineer is significantly higher than most job roles in the industry. However, various factors come into play when determining the salary. On average, a Big Data Engineer can earn between ₹4 lakhs and ₹35 lakhs per year or more with experience.
In this Big Data Engineer salary blog, we will discuss the salaries of these professionals in India. We will also examine how the salaries vary by experience.
Table of Contents:
What is Big Data?
Big Data is defined by its volume, variety, velocity, and veracity. This makes it crucial to have people who know how to handle it. As data grows, the field creates strong opportunities for Big Data Engineers and offers solid compensation for their work.
Now, let’s take a look at what a Big Data Engineer actually does.
What Does a Big Data Engineer Do? Roles and Responsibilities
Big Data Engineers possess the necessary skills to work with enormous arrays of complex datasets. As an organization’s dependency on data systems and tools has grown. The role of Big Data Engineers has become pivotal in the management and handling of these systems.
Big Data Engineers are responsible for utilizing available data and technologies. This is to build a strong data landscape for Data Scientists and analysts. Their knowledge extends to identifying suitable technologies, integrating data into central analysis infrastructure, and ensuring data accessibility across the organization.
Day-to-Day Tasks of a Big Data Engineer
A Big Data Engineer’s work typically involves understanding technical requirements, planning and development, and building a flexible but robust infrastructure. They work on data collection, storage, processing, and analysis.
Roles and Responsibilities of a Big Data Engineer
Key roles and responsibilities typically include:
- Raw data collection and processing at scale.
- Implementing suitable tools and frameworks for the design and development of data applications.
- Data reading, extraction, transformation, staging, and loading (ETL/ELT processes) to tools and frameworks as per requirements.
- Calling APIs, writing SQL queries, writing scripts, and web scraping.
- Coordinating with the engineering team for the integration of the Big Data Engineer’s work into the production systems.
- Processing unstructured data to make it into a suitable form for analysis.
- Analyzing processed data.
- Supporting business decisions with ad-hoc analysis.
- Monitoring data performance and modifying infrastructure as required.
- Defining data retention policies.
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Big Data Engineer Salary in India by Experience
Entry-level Big Data Engineers start with lower salaries, but experience and advancement into senior roles can lead to much higher pay. According to AmbitionBox, the annual average salary of a Big Data Engineer in India is ₹11 lakhs per year.
The table below shows how the salary range differs by experience level.
| Experience Level | Salary Range | Average Annual Salary |
| Entry-level (0-2 years) | ₹4 – ₹9 lakhs per year | ₹6 lakhs |
| Mid-level (3-7 years) | ₹8 – ₹16 lakhs per year | ₹10.8 lakhs |
| Senior-level (8+ years) | ₹9 – ₹35 lakhs per year or more | ₹19.9 lakhs |
Note: Salary may vary depending on experience, location, skills, industry, and company.
Average Big Data Engineer Salary Across Different Cities
Here are the average salaries offered to these Big Data professionals in the different cities of India:
| City Name | Average Annual Salary |
| Banaglore | ₹10.9 lakhs |
| Hyderabad | ₹10.3 lakhs |
| Chennai | ₹9.7 lakhs |
| New Delhi | ₹9.3 lakhs |
| Mumbai | ₹9.2 lakhs |
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Required Skills for a Big Data Engineer
Big Data Engineers work across programming, databases, and large-scale processing tools.
These are the core Data Engineer skills:
1. Foundational Machine Learning Knowledge
A basic understanding of Machine Learning helps when you work with large datasets. It makes sorting and processing data quicker, and helps you understand how models use that data. Engineers should know how to write simple Machine Learning algorithms and apply them during data ingestion.
2. Database Skills and Tools
Data storage, searching, and organization are the core of databases. Engineers need to understand how SQL and NoSQL systems work. NoSQL now includes a wide range of types, such as key-value cache, document store, and wide column store.
3. Hadoop
Hadoop is a set of open-source tools used to process massive datasets across many servers. Engineers should understand its modes, components and where it fits in a data pipeline.
4. Java
Java is widely used for data processing and for building Machine Learning related sequences. A solid command of it helps with automation and backend work.
5. Python
Python remains a core language thanks to its simplicity and large set of libraries. Engineers use it to build tools, scripts, and data workflows.
6. Apache Kafka
Apache Kafka handles real-time data streams and connects easily to external processing tools. Big Data Engineers know how its architecture works and how to integrate it.
7. Scala
Scala is common in data processing frameworks like Kafka and Spark. Its strong type system and concise syntax make it useful for large data applications.
8. Cloud Computing
The cloud drives most modern data systems. Big Data Engineers should know major platforms like AWS, Azure Data Lake, and Google Cloud. Also, understand cloud storage types, security levels, and available tools.
9. Hive
Apache Hive is a warehouse layer built on Hadoop that lets you run SQL-like queries at scale. Big Data Engineers should know how to query it and how its components work.
10. Apache Spark
Apache Spark is a key analytics engine for distributed data processing. Big Data Engineers work with its libraries, clusters, and APIs to run large-scale jobs.
Big Data Engineer Job Opportunities
The market for big data engineering is growing. As of today, there are over 4,000 Big Data Engineer job openings across India on LinkedIn and Glassdoor.
The international market, especially the United States market, lists over 9,000 jobs for Big Data Engineers on platforms like LinkedIn.
Big Data Engineer job opportunities (according to LinkedIn) in various Indian cities:
- Bangalore: Over 1,000
- Mumbai: Over 300
- Hyderabad and Pune: Over 700
- New Delhi: Over 200
The Bottom Line
The potential for a high-earning career as a Big Data Engineer in India is significant. Several factors, including location, experience, skills, and company, influence the salary range of a Big Data Engineer. By focusing on mastering high-demand tools like Spark and Hadoop, you can position yourself for a high-paying and future-proof career path.
Are you ready to take the next step in your Big Data career? Check out our Free Big Data Courses and gain the skills to become a certified Big Data Engineer.
| Related Blogs | What’s Inside |
| What is Hive? | Details Apache Hive as a SQL-based tool for data warehousing in Hadoop. |
| Hadoop vs Spark | Examines Hadoop versus Spark for big data processing capabilities and performance. |
| Splunk Tutorial | Outlines Splunk for analyzing logs and monitoring data in real time. |
| Cassandra vs MongoDB | Highlights differences between Cassandra and MongoDB for NoSQL database applications. |
| Spark vs MapReduce | Compares Spark and MapReduce for speed and efficiency in big data tasks. |
| Spark SQL | Describes Spark SQL for querying structured data in Apache Spark frameworks. |
| Hadoop Cluster | Explains the setup and functionality of Hadoop clusters for big data processing. |
| Apache Solr Tutorial | Guides on using Apache Solr for enterprise search and analytics solutions. |
| Hive vs HBase | Contrasts Hive and HBase for data management in Hadoop environments. |
Frequently Asked Questions
Q1. Is data engineering a high-paying career in India?
Yes, data engineering is one of the high-paying IT careers in India. Experienced professionals with in-demand skills can earn competitive salaries, especially in major tech cities like Hyderabad and Bangalore.
Q2. What is the average monthly salary of a Big Data Engineer in India?
The average monthly salary for a Big Data Engineer in India typically ranges from ₹30,000 to ₹60,000, though this is highly dependent on specific skills, total experience, and company.
Q3. Do I need specific certifications to get a higher salary?
Although it is not mandatory, having industry-recognized certifications such as AWS Certified Data Analytics or Google Professional Data Engineer can significantly boost your resume. It also provides an edge in interviews and often leads to higher salary offers.
Q4. What is the difference between a Data Engineer and a Data Scientist?
A Data Engineer focuses on building and maintaining the infrastructure and reliable data pipelines that collect, process, and store data. A Data Scientist then uses that clean, accessible data to perform analysis, build predictive models, and extract business insights.
Q5. Which skills have the biggest impact on a Big Data Engineer's salary?
Top skills that have the biggest impact on a Big Data Engineer’s salary include Python and SQL, strong knowledge of cloud platforms (AWS, Azure, GCP), and expertise in Big Data technologies like Apache Spark, Hadoop, and Kafka.