Your job as a data engineer is to set up databases and data warehouses and make them as efficient as possible for storing and retrieving data. 

Data engineers use hybrid, NoSQL, or SQL infrastructures to assist businesses in making sense of vast amounts of data.

Massive volumes of data can be collected, stored, and analysed more easily than before. As a result, there is now a greater need for database engineers with the necessary knowledge and experience. 

Data engineers frequently receive yearly salaries of above $110,000, according to Glassdoor.

What data engineers perform, factors that may affect their wages, and how to start on this fascinating career path are all covered in this book.

What does a big data engineer do?

A prominent data engineer is responsible for designing, building, maintaining, and troubleshooting the data pipelines that allow organisations to store, process, and analyse large datasets. 

This often involves working with big data technologies like Hadoop and Spark and using programming languages like Java, Python, and SQL to build custom data processing systems.

Data engineers work closely with data scientists, business analysts, and other data professionals to understand the data needs of an organisation and design solutions to meet those needs. 

They may also implement governance policies, ensure data quality, and integrate data from various sources.

Some specific tasks that a data engineer might be responsible for include:

• Extracting data from various sources, including databases, APIs, and flat files
• Transforming and cleaning data to make it ready for analysis
• Loading data into data warehouses and other storage systems
• Building and maintaining data pipelines using tools like Apache Beam, Apache Flink, and Apache NiFi
• Developing custom scripts and software to process and analyse data
• Collaborating with data scientists and other data professionals to understand data requirements and design solutions
• Implementing data governance policies to ensure data quality and security
• Troubleshooting and debugging data pipelines as needed

How much does a big Data Engineer earn?

A data engineer earns an average of $111,998 a year, including base pay, bonus pay, and profit-sharing compensation, according to Glassdoor. 

In contrast, senior data engineers have an average yearly salary of $154,989. As a data engineer, you can often anticipate making a higher-than-average compensation.

Total pay is the sum of the average salary reported by Glassdoor users and any additional compensation, such as profit-sharing, commissions, cash bonuses, or tips.

Factors affecting the salary of a Data Engineer

Several factors can affect the salary of a data engineer. These include:

• Education and experience: Data engineers with higher levels of education, such as a master's degree in a related field and more experience in the area, are typically more in demand and may command higher salaries.
• Industry: Data engineers in finance, healthcare, and technology tend to have higher salaries than those in other industries.
• Location: Data engineer salaries can vary significantly by location. Data engineers working in major cities or tech hubs like San Francisco, New York, and Seattle tend to earn higher wages than those working in smaller towns or rural areas.
• Company size and type: Data engineers working for larger companies or well-known tech firms may earn higher salaries than those working for smaller startups or non-tech companies.
• Skills and expertise: Data engineers with specialised skills or expertise, such as experience with specific big data technologies or programming languages, may command higher salaries.
• Job market demand: The demand for data engineers can vary depending on the job market and the specific needs of employers. In times of high demand, data engineers can negotiate higher salaries.

It's important to note that these are just a few factors that can affect the salary of a data engineer.

Many other factors can come into play, so it's always a good idea to research salary data for your specific location and industry to get a more accurate picture of what you can expect to earn as a data engineer.

Job title variations and salary

Several job titles are similar to or overlap with the role of a data engineer, and these titles may come with different salary expectations. Some standard job titles in this field include:

• Data Scientist: Data scientists analyse and interpret data to extract insights and inform business decisions. They often have a solid statistics and machine learning background and may work closely with data engineers to develop data pipelines and prepare data for analysis. According to Glassdoor, the median annual salary for a data scientist in the United States is $122,000.
• Big Data Engineer: Big data engineers are responsible for designing, building, and maintaining data pipelines to process and analyse large datasets. They often work with technologies like Hadoop, Spark, and NoSQL databases and may have a background in distributed systems and data storage. According to Glassdoor, the median annual salary for a big data engineer in the United States is $121,000.
• Data Architect: Data architects are responsible for designing and implementing the overall data infrastructure of an organisation. They work closely with data engineers to develop data pipelines and storage systems and may also be responsible for data governance and security. According to Glassdoor, the median annual salary for a data architect in the United States is $119,000.
• Software Engineer: Software engineers are responsible for designing, developing, and maintaining software systems. While software engineering and data engineering can overlap in some cases, software engineers may not necessarily work with big data technologies or be responsible for designing data pipelines. According to Glassdoor, the median annual salary for a software engineer in the United States is $107,000.

It's worth noting that these are just a few job titles related to data engineering, and many other titles may be used in this field. 

In addition, salary expectations can vary widely depending on location, industry, and experience. 

As such, it's always a good idea to research salary data for your specific location and job title to get a more accurate picture of what you can expect.

How to become a Big data engineer

To become a big data engineer, you must have a strong computer science, programming, and data management background. 

Depending on your career goals and the job you are interested in, you may also benefit from education and training in other related fields, such as statistics, machine learning, or data visualisation. 

Some specific steps you can take to become a big data engineer include:

• Earn a bachelor's degree in computer science or a related field: A bachelor's degree in computer science or a related field, such as data science or electrical engineering, can provide a solid foundation in the technical skills and knowledge you will need to work as a big data engineer.
• Gain experience with big data technologies: To work as a big data engineer, you must be proficient in technologies like Hadoop, Spark, and NoSQL databases. You can gain experience with these technologies through internships, online courses, or self-study.
• Learn programming languages like Java and Python: Big data engineers often use programming languages like Java, Python, and Scala to build custom data processing systems and scripts. It's a good idea to become proficient in at least one of these languages to increase your chances of being hired as a big data engineer.
• Get certified: Several certifications can help you demonstrate your skills and knowledge as a big data engineer. Some standard certifications in this field include the Cloudera Certified Developer for Apache Hadoop (CCDH), the Cloudera Certified Data Engineer (CCDE), and the Hortonworks Certified Apache Hadoop Developer (HDPCD).
• Build a portfolio of projects: One of the best ways to demonstrate your skills and experience as a big data engineer is to build a portfolio of projects that showcase your ability to design and implement data pipelines. You can do this through internships, personal projects, or by contributing to open-source projects.
• Keep learning: The field of big data engineering is constantly evolving, so it's essential to stay up to date with the latest technologies and best practices. You can do this through online courses, attending conferences and meetups, and staying active in the big data engineering community.