Useful Data Engineer Skills You Can Develop (With Steps)
Updated 30 September 2022
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Many companies hire data engineers to gather, process and analyse information present in large volumes of data sets. As one of the professions with rapid growth, data engineers are generally in high demand. Reviewing the required skills can be beneficial if you want to pursue a career in the technology sector. In this article, we discuss several important data engineering skills and explore the steps necessary to become a data engineer and their duties along with salary.
What are data engineer skills?
Data engineer skills typically incorporate various aspects of software engineering with that of data science to build and manage the data infrastructure in several projects. Executives in a variety of businesses apply this data to develop strategic and financially beneficial decisions. Data engineering is a competitive field and requires a unique skill set, including highly technical skills like handling databases and soft skills like interpersonal or communication skills. Their skills enable them to monitor that data flows between servers and applications smoothly and to check for irregularities.
Related: Data Scientist Skills (With Examples And Tips To Improve)
Skills necessary to become a competent data engineer
Most data engineers acquire and develop the skills listed below to carry out their job successfully:
To make use of technologies, such as big data or machine learning, data engineers may often run commands in software and applications. This is why data engineers may require knowing how to program. It is crucial to be adept in programming and to have in-depth knowledge of programming languages is helpful. Coding is a sought-after ability that is necessary for most data engineering jobs. Many organisations expect candidates to possess a working knowledge of programming languages such as Python, C, C ++, Perl or Scala.
Read more: What Is Coding? A Complete Guide To Coding Languages
Knowledge of database systems
The SQL programming language is the industry standard for creating and maintaining a relational database. A relational database is an organised collection of data elements linked by pre-defined connections, comprising tables with rows and columns. Non-tabular databases (NoSQL) come in many kinds, based on different types of data models, like a document. Knowledge of various database management systems (DBMS), a type of software application that allows interaction between databases for data storage and retrieval, is necessary for data engineers.
Read more: Relational Database Management Systems: MSSQL Vs. MySQL
Understanding data warehousing systems
Data warehouses hold massive amounts of data for querying and analytic purposes. This information comes from a variety of places, including customer relationship management systems, accounting systems and enterprise resource planning systems. Many businesses may use this data for reporting, analytics and data mining. Most companies expect entry-level engineers to be conversant in various cloud services platforms and the array of data storage tools that come with them.
Read more: All You Need To Know About Data Warehouse Architecture
Expertise in ETL tools
Extract, Transform, Load (ETL) is the process of extracting data from a source, converting (transforming) it into an analysable format and storing or loading it into a data warehouse. Data engineers use batch processing to assist companies in analysing data relevant to a particular business challenge. The ETL collects data from a variety of sources, applies business rules to the data and then loads the retrieved data into a database or business intelligence platform where everyone in the organisation can access and use it.
Understanding distributed systems
One of the most crucial data engineer skills is proficiency in distributed systems like Hadoop. The Apache Hadoop software library is a system that offers basic programming principles to enable the distributed processing of massive data volumes across multiple devices and servers. Data engineers can use this system to expand from a central server to thousands of devices, each with its computation and storage capabilities. Most organisations look for data engineers that are well-versed in analytics tools, particularly Apache Hadoop-based solutions.
Machine learning skills
Data engineers use machine learning algorithms, often known as models, to create predictions based on present and past data. They simply require a basic knowledge of machine learning to better understand the needs of data scientists to deploy models and construct more reliable data pipelines. Developing your data modelling and data analysis skills can help you design solutions that others can use, establishing you as a valued resource for any firm.
Knowledge of application programming interface (API )
A software application's set of definitions and protocols to manage its data refers to as an API. It enables two apps or devices to communicate with one another to accomplish a specific job. Web applications, for example, employ API to interface with back-end functionality and data from the user-facing front end. When a website visitor makes a request, an API allows the programme to scan the database, obtain information from the database, execute the request and return an HTTP-based result to the web template and show it in the browser. Data engineers provide APIs so that data scientists and BI analysts can query the information.
Related: Essential Web API Interview Questions And Example Answers
Understanding algorithms and operating systems
Although data engineers primarily focus on data preprocessing and streamlining, reviewing the company's operational data functions is also important. Defining milestones and making long-term goals for the business problem or situation requires a fundamental understanding of algorithms. It is also critical for a data engineer to have an in-depth understanding of operating systems.
Presenting and communicating skills
Data engineers may conduct data analysis and convey their findings to stakeholders, depending on the size of the data science team. A data engineer can become an engaging presenter by mastering public speaking and communicating technical data principles in the context of addressing a business problem. This might raise the likelihood of proper implementation of their suggestions. As a data engineer, you may often collaborate with both technical and non-technical counterparts in your office, so excellent communication skills are essential. Data engineers usually present their results and recommendations to colleagues who lack technical expertise.
As people rely on one another for results, a strong interpersonal workplace relationship is necessary to keep projects on schedule. Data engineers are generally aware of the requirements of the teams with whom they are collaborating and what challenges they face. Understanding where this effort fits into the business allows data engineers to aid other teams and come up with better collaboration ideas.
Critical thinking skills
Data engineers generally possess and utilise a diverse set of technical skills. The position necessitates critical thinking and the capacity of solving issues creatively. Data engineers can assess problems and then devise effective and innovative solutions. This can entail coming up with technologies that do not yet exist in the market.
How to become a data engineer
Follow the steps given below if you want to become a data engineer:
1. Get a bachelor's degree
Most companies demand that data engineers at least have a bachelor's degree. A degree in information technology, computer science, software engineering, applied mathematics, statistics or a related discipline is desirable, but not mandatory. If you want to pursue a degree that isn't in one of these fields, you might consider taking coding, database administration, algorithms or data structures classes too.
2. Enrol in certification programmes
Gaining certifications in data engineering is especially helpful. You can use these to demonstrate your proficiency in the field and prove your expertise to potential employers. You can either enrol for these specialised courses and opt to study online while pursuing your degree or choose a full-time program.
3. Improve relevant skills
Consider working on personal projects to further your knowledge of the area and enhance your knowledge of crucial solutions and programming languages like SQL and Python. You can begin by enhancing your programming skills and studying the relevant languages. You may consider learning the syntax and operations of the scripting language and product specifications, such as operational processes, escalations and actions. Data engineers work with both organised and unstructured data in databases, so developing your skills in data processing and data warehousing is necessary. Staying up to date on the latest technologies is a great way to develop your skills.
4. Complete an internship
Internships are a terrific way to broaden your skill set while also learning on the job and getting significant experience. You can work with mentors on different projects and learn from their expertise. Consider including these experiences in your portfolio to demonstrate your abilities to potential employers and add value to your resume.
Please note that none of the companies mentioned in this article are affiliated with Indeed.
Salary figures reflect data listed on Indeed Salaries at the time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.
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