Big Data Engineer Salary

Big Data, an exponentially growing information trove, challenges traditional data management with its vastness and complexity. Characterized by volume, velocity, variety, veracity, and value, it requires advanced tools like Apache Spark and Hadoop for effective handling. This field, encompassing everything from structured to unstructured data, is evolving rapidly, offering significant opportunities for data scientists and engineers to manage and analyse this immense data landscape. Let's explore the dynamic world of Big Data.

What does a Big Data Engineer Do?

An organization's data infrastructure is developed, managed, and maintained by Big Data engineers. They accomplish this by collecting, extracting, and transforming large amounts of data from different sources and storing it in databases in a structured format that makes it easier for stakeholders to use.

Big Data Engineer Tasks

• The Big Data engineer works with the data infrastructure of an organization and implements data processing systems to process the data.
• They extract, transform and load the data into data storage systems for easy access by multiple departments of the organization.
• They prepare machine learning pipelines for analyzing data and finding useful meaning from the data.
• They create data pipelines to easily handle large amounts of data for future analysis.

Required Skills of a Big Data Engineer

• One of the basic skills of a Big Data engineer is a clear understanding of machine learning and knowledge of various machine learning algorithms. A Big Data engineer will not create new machine learning models but will utilize machine learning models.
• Knowledge of handling data from structured (spreadsheet, MySQL), unstructured (text, video, audio, MongoDB), and semistructured (emails, XML) databases.
• Usage of query and advanced SQL commands to model the database and fetch the required data from the database.
• Having knowledge of Hadoop for processing large datasets across a large number of scalable servers and devices at the same time.
• Understand the usage of Apache Spark for batch processing of large datasets and other applications like graph processing, real-time data processing, and interactive data analysis.
• Frameworks like Apache Flink for streaming data processing with low latency, Apache Storm, a real-time computation engine for processing large streams of data in real-time, and Apache Beam for batch and streaming data processing.
• Experience with tools like Kubernetes to containerize and manage the deployment of machine learning models.
• Programming languages like Python, Java, and Scala are also important skills as multiple machine learning libraries are written in Python. Java can be used with multiple tools like Hadoop, Spark, Flink, Kafka, and graph processing through Apache Giraph and Neo4j. Scala could be used to perform data processing with libraries like Spark MLlib, Kafka, etc.
• Apache Kafka is a Java and Scala-based distributed processing platform that can be used to connect to any external processing library for working with real-time data streams.
• Understanding of Apache Hive to utilize data warehouses built on top of Hadoop for data queries. Like SQL, it supports indexing, user-defined functions, and metadata storage.

Roles and Responsibilities of a Big Data Engineer

The major role of a Big Data engineer involves creating, maintaining, and ensuring a production-ready Big Data environment that is easily accessible whenever data is needed.

• Creating, constructing, and maintaining large-scale data processing systems for the collection of raw data from different sources and processing at scale. This can also be called ETL (Extract, Transform, Load) which stands for extracting the data from various resources and transforming the data using preprocessing to load into data warehouses.
• Implementation of SQL queries to get data from the database and development of algorithms for processing data.
• Transform collected data into meaningful and valuable information by implementing technical processes and business logic through the integration of machine learning models into a production system.
• Creating data pipelines and deployment pipelines to automate the processing and storage of data. Evaluate, compare, and improve the data pipeline for better productive outcomes.
• Data performance monitoring and analysis to support business decisions.
• Ensuring that the data satisfies the quality, governance, and compliance standards for trustful usage in operational and business activities.
• Understanding of cloud management to define data retention policies for long time storage of unused data.

Big Data Engineer Salary in India

After analyzing data from multiple sources, it can be concluded that the average salary for a Big Data engineer in India is approximate ₹820,000 per year. The salary may vary depending on the city and level of experience. For instance, at an entry level, a Big Data engineer can expect to earn around ₹466,265 per year. With 1 to 4 years of experience, a Big Data engineer can earn approximately ₹722,721 annually. After 5 to 9 years of experience, Big Data engineers can earn upwards of ₹1,264,555 per annum. Finally, senior Big Data engineers in India can earn an annual salary of around ₹1,681,640.

The salary also varies according to the industry, for example, banking and retail industries provide high salaries for Big Data engineers than any other industries because of the large need for data processing and analytics.

Average Salaries Across Different Cities

The Big Data engineer salary in India across major cities are:

• Companies like Dell, Accenture, and Amazon in Bengaluru, Karnataka are involved in developing and implementing data-driven solutions for clients, and a Big Data engineer's salary in these companies is around ₹897,000 per annum.
• Multiple companies like Wipro, TCS, CTS, and HCL in Chennai, and Tamil Nadu are developing data processing solutions, and a Big Data engineer's salary in such companies is around ₹1,171,000 per annum.
• Companies like Tech Mahindra, IBM, and Accenture in Delhi are working on data analytics, and a Big Data engineer's salary in such companies is around ₹820,000 per annum.
• Mumbai, Maharashtra also has many Big Data firms like Capgemini and Infosys, and a Big Data engineer's salary in these companies is around ₹811,000 per annum.
• Hyderabad, Telangana is home to companies like Oracle, Amazon, and Microsoft that perform Big Data analytics, and a Big Data engineer's salary in Hyderabad is around ₹946,000 per annum.

Big Data Engineer Job Opportunities

Every organization is starting to understand the need for processing or analyzing data and adopting data analytics and management environment. Therefore, the demand for Big Data engineers is continuously growing. There are almost 4000+ Big Data jobs on LinkedIn in India. Statistics show that to gain a competitive edge, $83\%$ more companies are creating Big Data infrastructures to handle a large amount of data and there has been a $123\%$ increase in Big Data engineer jobs since 2015.

Big Data engineers with skills in SQL, Python, Hadoop, and ETL pipelines are in high demand compared to other Big Data engineers. As businesses continue to collect and process large volumes of data, the need for skilled Big Data engineers will only increase. Therefore, if you have the right skills there is always a job opportunity in the field of Big Data engineering.

Additional Resources

1. Application of Big Data