How To Become A Big Data Engineer (With Essential Skills)

Updated 9 July 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Big data engineers design, build, test and maintain complex data processing systems with large data sets. These professionals interpret massive amounts of data daily to predict patterns and behaviour to help understand organisational and consumer behaviour. Knowing what data engineers do and the certifications and requirements to become one can help you identify whether this career path is right for you. In this article, we discuss the meaning and duties of a big data engineer along with steps you can take to become one if data sets intrigue you.

What is a big data engineer?

Big data engineers are computer professionals who analyse extensive data sets daily to understand and predict organisational practices and behaviour and identify areas that can benefit from improvement. These professionals interact with massive data processing systems and databases in large-scale computing organisations to help improve the overall performance of employees in various departments by identifying trends and practices helpful for growth.

Sound big data engineers provide organisations with accurate analyses that can help them assess their overall company performance, identify market demographics and predict any upcoming changes or trends in the industry. Almost every field or market requires efficient big data engineers to produce and benefit from valuable insights. Big data engineers work in multiple sectors, including business, finance, government, healthcare, science and telecommunications.

How to become a big data engineer in four steps

Follow these four steps to learn how to become a big data engineer:

1. Earn a bachelor's degree

The first step to becoming a big data engineer is getting a bachelor's degree in computer science, mathematics, data science, business analytics or a related field. Depending on the degree course, earning a bachelor's degree can take three or four years. These degrees equip the candidates to analyse and interpret data using programming languages and train them to use big data SQL engines for data management and analysis. A bachelor's degree prepares the graduates for an advanced career in data analytics and engineering by providing them with a workable knowledge of essential concepts such as database architectures and SQL.

2. Gain relevant experience

Gaining relevant work experience even while pursuing higher education in data engineering and analytics can help students develop the skill set required for a successful data engineer. Working in a suitable organisation can ensure that you continue to develop essential technical and soft skills such as communication, problem-solving, analytical skills, critical thinking skills and attention to detail. Try experimenting with various aspects of the job role and decide the areas you like working the most on while developing additional skills to complement your interests. Working with experienced IT professionals can help improve your subject understanding and develop essential collaborative skills.

3. Get additional certifications

If you want to enhance your career prospects and attract better opportunities that allow you to grow professionally, consider getting additional certifications to upskill your knowledge and understanding. As an aspiring big data engineer, you can consider enrolling in many certificate programs, paid or unpaid, and even learn online. Employers may consider your candidature favourably if you show them proof of relevant skillset and experience through additional certifications. These certifications can set you apart from others and position you as a committed, proactive and passionate professional keen on doing impactful work as a data engineer.

Here are some popular professional certifications you can consider enrolling in to enhance your functional knowledge of data engineering:

Cloudera certified professional (CCP) data engineer: This certification trains professionals to become more proficient in data analysis, workflow development, data ingestions, data staging, storage and transformation. You can enrol in this certification for 29,667 INR and appear for the four-hour exam to get certified.
Certified big data professional (CBDP): The CBDP certification focuses on testing a candidate's proficiency in data science and business intelligence. The Institute for certification of computing professionals (ICCP) developed this certification, and the cost varies with the level of the examination.
Google cloud certified professional data engineer: This certification test an individual's proficiency and competence in building data structures, developing data systems and analysing and designing machine learning. The certification exam lasts for about two hours and cost 14,830 INR.

4. Pursue higher education

At this stage, you can consider enrolling in a master's or a related program in business analytics, data science or a related field to complement your experiential learning. A higher education degree program can help enhance your understanding and functional knowledge of data science in an up-to-date manner with the latest industry trends. Enrolling in higher education can also expose nuanced analyses techniques and new methods to help you perform better while providing a solid network of professionals. The duration of your degree may vary depending on specialisation and the nature of the program.

Role of a big data analyst

Some typical roles of a big data analyst include:

implementing and maintaining software systems, including the designing, logistical and execution phase
performing the ingestion, storage and processing of large amounts of data daily
designing the architecture of big data platforms
maintaining data pipeline and implementing extract transform load operations or ELT operations
customising and managing various integration tools, such as databases, warehouses and analytical systems
conducting performance optimisation
setting up data access tools for the organisation's data scientists
setting up and managing steaming flows
researching new methods to collect data with improved quality
working with data scientists and analysts to devise effective strategies for business development
building efficient business models

Essential skills for a big data engineer

Following are some essential skills for big data engineers to excel at their jobs:

Data warehousing

One of the primary functions of big data engineers is to perform ELT operations, so a working knowledge of developing and using data warehouses can be beneficial. Since the job of a data engineer requires them to analyse massive amounts of data, which is why familiarity using popular warehousing solutions, such as Panoply and Redshift, can be crucial to effective performance. Big data engineers are proficient in tuning production strategies and performing customer and operations analysis using data warehousing. Therefore, having sound knowledge of various warehousing software and solutions can help ensure job efficiency and accuracy.

Database systems and SQL

Big data engineers handle enormous data to perform analyses and identify trends. Knowledge of various database systems and their management can be crucial to job success. Most data engineers use the structured query language (SQL) to study data and employ solutions. Having an in-depth knowledge of this language can help support their database management function. Data engineers can benefit from knowing how to write SQL queries for multiple relational database management systems (RDBMS). Commonly used database management systems include MySQL, Oracle database and the Microsoft SQL Server.

Data analysis

Data engineers interpret extensive data to help predict behaviours and trends, so familiarity with data analysis techniques, practices and analytics software can help these professionals perform their job duties effectively. Big data engineers use various static modelling techniques to summarise relevant data through descriptive analysis. These professionals work closely with data scientists and analysts to interpret processes, integrate the findings and produce results to improve company competitiveness. Data engineers are proficient in using popular analytics software, including Apache Hadoop-based solutions like MapReduce, Pig and HBase.

Critical thinking

Another necessary aspect of a data engineer's job is examining and analysing data using their strong attention to detail and critical thinking abilities. Being a critical thinker enables these professionals to strategically look at a situation from different viewpoints and devise the most workable, creative, and effective solution. Big data engineers face challenging conditions during data loss or replication, so the ability to look through challenges and think critically to provide alternative ways of analysing data is crucial to the job. These professionals work with hypotheses and develop solutions to problems that don't exist where this skill can be helpful.

Communication

Good interpersonal and communication skills can help big data engineers perform their job efficiently. Since data engineering is a collaborative job where these professionals work with various people, such as data scientists, engineers, and analysts, good interpersonal skills allow them to convey their ideas effectively. Communication entails written and verbal communication abilities as data engineers prepare data and analyses reports while sharing the findings with relevant stakeholders, requiring strong communication skills. Excellent communication skills allow these professionals to effectively discuss complex data reports and make them understandable for a non-technical audience.

Programming

Data engineering is a technical field, and programming knowledge is an essential requirement for all professionals who work in this profession, as their job requires them to develop codes and analyse data. Big data engineers can benefit from a working knowledge of any popular programming language, such as Java, C++ or Python. Being proficient in these languages can help data engineers perform their job duties smoothly and quickly.

Please note that none of the companies mentioned in this article are affiliated with Indeed.