What is data engineering? A career guide for job seekers

What is data engineering? A career guide for job seekers

Anyone looking to pursue a career in data engineering should have a good grasp of what the role entails and why it’s a lucrative and in-demand career option. Data engineers work with raw data, transforming it into more usable formats, then storing it ready for when it’s needed.

Processing data in this way makes it widely accessible for a variety of customers and industries, enabling them to make informed decisions based on facts. The analytical community, including data scientists and digital performance analysts, rely on such data to ensure organisations are performing at the optimum, making a data engineer a crucial role.

Creating scalable and cost-effective data products has become so critical to success that data engineering is among the most in-demand disciplines in the UK. Such a fast-growing area shows no sign of abating as society becomes more and more digitalised, reinforcing the wisdom of following a data engineer career path.

Key responsibilities of a data engineer

Like all roles, each data engineer will have slightly different responsibilities, but the main ones are likely to include:

• Data pipeline development
• Acquiring relevant datasets
• Developing data streaming systems
• Database management
• Ensuring data quality
• Creating new systems for data analytics
• Streamlining business intelligence operations
• Writing business intelligence reports
• Developing algorithms to transform data
• Collaborating with other teams on company objectives
• Ensuring compliance with data governance and security policies

A typical day in the life of a data engineer starts with data pipeline check-ups, as these processes are left running overnight and any malfunctions must be resolved before moving on to other work. Errors can take between 15 minutes or a whole day to rectify, helping to keep the role varied.

The next task to move onto is building data pipelines or amending existing ones, as moving data is now a key function of businesses that rely on analytics for their processes. After this, data engineers will likely investigate whether there are any improvements available that would make current infrastructure work any better.

Despite having specialist data engineering skills, there are some elements of a job that are fairly ubiquitous. Part of the day will usually be taken up with administrative tasks, answering emails and attending meetings to ensure everything runs smoothly.

Important skills for data engineers

The skills possessed by data engineers fall into two categories: technical and soft skills. While much of a candidate’s training will focus on the former, the latter should not be overlooked. In fact, knowing multiple programming languages will not set a job seeker apart if their problem-solving skills are not up to scratch.

Essential technical skills for a career in data engineering include:

• Proficiency in coding languages like SQL, NoSQL, Python, Java, R and Scala.
• Knowledge of relational and non-relational databases, as common solutions for data storage. 
• Aptitude in using extract, transform, and load (ETL) tools, such as Xplenty, Stitch, Alooma and Talend.
• Ability to differentiate between data solutions, knowing when to use a data lake versus a data warehouse, on occasion.
• Confidence in writing scripts to automate repetitive tasks.
• Implementing operational system data flows.
• Understanding the basic concepts of machine learning in order to work well with the data scientists in the company.
• Working knowledge of big data tools like Hadoop, MongoDB and Kafka.
• Ability to use cloud platforms including Amazon Web Services (AWS) and Google Cloud Platform (GCP).
• Securely managing and storing data to protect it from infiltration.

Soft skills data engineers should really develop are:

• Problem-solving
• Teamwork within the department and with others
• Adaptability
• Communication with non-technical audiences

Tools and technologies in data engineering

There are many tools for data engineers, which can improve efficiency and scalability when completing data processing and data management. Consider how these technologies will integrate with others being used by the team you’re joining.

The options include:

• Apache Spark – an open-source analytics tool that can be used for big data applications due to its sophisticated capabilities
• Apache Kafka – a distributed stream processing platform, which enables data engineers to build data pipelines in real time and create interactive data applications 
• Apache Airflow – an open-source platform to schedule, manage and monitor complex data pipelines
• Snowflake – cloud-based data warehousing tool that stores, manages and analyzes large volumes of data
• Tableau – a solution that functions both as a data engineering and business analysis tool, combining the two to create visual data metrics
• BigQuery – a tool that stores large amounts of data and can perform immediate analysis and processing 

Data engineers can use tools and technologies to visualise data pipeline dependencies, check logs, trigger tasks and test data quality. Not only can these speed up these tasks, but it can also make them more accurate, leading to more useful data in the long run.

Career path and growth opportunities

Data engineers often come from a background in computer science, data analytics or software engineering. Having a bachelor’s degree in a relevant subject is a good place to start and while a master’s degree can also be useful, taking industry-accredited courses will help to ensure you have the right skills to carry out the role successfully.

Entry-level data engineering jobs don’t really exist, as the discipline requires an extensive knowledge and broad skillset. This is why candidates often pivot from another career such as data analyst into the role. Data engineers can find interesting growth opportunities training artificial intelligence and machine learning models in today’s jobs marketplace.