What Is a Data Engineer?: A Guide to This In-Demand Career

The process of creating and developing systems for mass data collection, storage, and analysis is known as data engineering.

It is a wide field having applications in nearly every sector of the economy. Massive volumes of data can be collected by organisations; however, in order to guarantee that this data is highly useable by the time it reaches data scientists and analysts, they require the appropriate personnel and technology.

By working as a data engineer, you can not only make a real difference in a world where 463 exabytes will be produced daily by 2025 , but also make the life of data scientists easier.

That is one and eighteen zeros of information. Without data engineers to handle and organise the data, fields such as machine learning and deep learning cannot flourish.

What does a data engineer do?

Building systems that gather, organise, and transform unprocessed data into comprehensible information for data scientists and business analysts to evaluate is the responsibility of data engineers in a variety of contexts.

Making data accessible is their ultimate objective so that businesses may utilise it to assess and improve their performance.

These are some common tasks you might perform when working with data:

  • Acquire datasets that align with business needs
  • Support the development of data streaming systems
  • Implement new systems for data analytics and business intelligence operations
  • Develop business intelligence reports for company advisors
  • Develop algorithms to transform data into useful, actionable information
  • Build, test, and maintain database pipeline architectures
  • Collaborate with management to understand company objectives
  • Create new data validation methods and data analysis tools
  • Ensure compliance with data governance and security policies

Working for smaller businesses frequently entails playing a generalist position and handling numerous data-related duties.

Data engineers that specialise in creating data pipelines work for certain larger corporations. Some, on the other hand, concentrate on managing data warehouses, which includes building table schemas to keep track of data storage locations and filling warehouses with data.

Why pursue a career in data engineering?

A career in this area has the potential to be both fulfilling and difficult. You’ll be crucial to the success of a company by making data that data scientists, analysts, and decision-makers need to perform their duties easy to access. To develop scalable solutions, you’ll need to use your programming and problem-solving abilities.

With the government of India advancing the Digital India plan and encouraging digital growth as a crucial component of India’s economic future, the growing significance of data in our society has garnered national attention in that country. The demand for experts who can comprehend, handle, and evaluate data is anticipated to rise as long as it remains a fundamental component of many sectors.

Data engineer salary

Because it requires advanced training and a high level of technical skill, data engineering is a well-paying vocation. Glassdoor reports that the average wage in India is ₹9,07,000. The factors that affect an individual’s pay are location, experience, size of the organisation, and work responsibilities.

Data engineer career path

Data engineering is only sometimes an entry-level role. Instead, many data engineers start as software engineers or business intelligence analysts. As you advance, you may move into managerial roles or become a data architect, data scientist, or chief data officer.

How to become a data engineer

With the correct set of skills and knowledge, you can launch or advance a rewarding career in data engineering. Many data engineers have computer science, information technology, or applied math backgrounds. A formal degree, such as from a university or college, can help build a strong quantitative foundation to master data and infrastructure tasks in this quickly-evolving field. It is also popular to earn a postgraduate degree when pursuing a career in data engineering to advance your career and unlock potentially higher-paying positions.

1. Develop your data engineering skills.

Learn the fundamentals of data management, integration, modelling, testing, and engineering to increase your chances of success in a career in data science. Several technical skills to consider honing your skills in include:

  • Coding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practise your skills. Common programming languages include SQL, NoSQL, Python, Java, R, and Scala.
  • Relational and non-relational databases: Databases rank amongst the most common solutions for data storage. You should be familiar with both relational and non-relational databases and how they work.
  • ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
  • Data storage: Some types of data should be stored differently, especially in big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse.
  • Automation and scripting. Automation is necessary to work with big data simply because organisations can collect so much information. You should be able to write scripts to automate repetitive tasks.
  • Data analytics and business intelligence systems: Implementing operational system data flows.
  • Machine learning: While machine learning is more the concern of data scientists, it can be helpful to grasp the basic concepts better to understand the needs of data scientists on your team. 
  • Big data tools: Data engineers don’t just work with regular data and often manage big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka.
  • Cloud computing: You’ll need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.
  • Data security: While some companies might have dedicated data security teams, many data engineers securely manage and store data to protect it from loss or theft.
  • Presenting findings to non-technical audiences: Be able to explain what you are designing or fixing and how this will benefit the organisation.

2. Get certified. 

A certification can validate your skills to potential employers, and preparing for a certification exam is an excellent way to develop your skills and knowledge. Options include internationally recognised Professional Certificates such as the Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, or Google Professional Data Engineer.

Check out some job listings for roles you may want to apply for. If you notice a particular certification is frequently listed as required or recommended, that might be an excellent place to start.

3. Build a portfolio of data engineering projects. 

A portfolio is often a key component in a job search, showing recruiters, hiring managers, and potential employers what you can do.

You can add data engineering projects you’ve completed independently or as part of coursework to a portfolio website. Alternatively, post your work to the Projects section of your LinkedIn profile or to a site like GitHub — both free alternatives to a standalone portfolio site.

4. Start with an entry-level position. 

Many data engineers start in entry-level roles to build the experience and skills needed for more advanced roles. Roles such as database administrator or database developer can help you build relevant skills and gain industry experience. As you build knowledge and learn from other experts in the field, you will be more equipped to grow within your role and transition into more advanced data careers.

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