Data engineer roadmap: Key steps for a successful career

Planning a career in data engineering has never been more sensible, as it’s a growing field, with companies becoming more and more reliant on data each day. Being able to manage and process data at scale will make you invaluable to a business, with this demand being reflected in salaries averaging around £50,000 a year.

In order to fulfill your potential in this area, it’s worth working out a roadmap for data engineering to keep your career on track. That way, you can navigate the route to becoming a data engineer with relative ease and advance your skills to set you up for the future.

Step 1: Familiarise yourself with the basics of data engineering

Your data engineering skills will evolve and be added to over time, but in order to secure your first role in the field, you need to have a good grasp of the essentials. These are the four basics of data engineering you cannot overlook:

Introduction to database management

Any modern data engineer should understand the fundamentals of databases and explore the ways in which they are used. Learn to tell the difference between types of database management systems before practicing basic creation and data selection in each one.

Python

Python may not be the only programming language you master during your career as a data engineer, but it’s a good place to start, as it’s relatively easy to learn and understand. It will enable you to write transformation jobs and deploy, validate and test scripts.

SQL

Structured Query Language (SQL) is the data industry’s method of communicating with a relational database and is used by data engineers, data scientists and data analysts. With it, you’ll be able to select, insert, update and delete data.

ETL concepts

ETL (extract, transform, load) is a core data engineering process, which facilitates moving data from multiple sources to a data warehouse. Understanding the different stages of ETL and having knowledge of the tools used for data integration is a crucial part of the job.

Step 2: Learn the appropriate tools and technologies

There are certain data engineering tools and technologies that you’ll use all the time in this career. It’s important to start learning them early and to keep up-to-date with new versions or any emerging developments that could help to streamline the process further.

Essential tools include:

• Apache Spark - an open-source unified analytics engine, which is used for data processing on a large scale. It has a useful interface for programming clusters with implicit data parallelism and fault tolerance.
• Hadoop - an open-source framework that uses distributed storage and parallel processing to break down large workloads into smaller ones for the analysis of massive datasets. This makes it more time efficient.
• Kafka - a distributed stream processing platform that facilitates the creation of real-time data pipelines and interactive data applications.
• Cloud platforms - like AWS, Azure, and GCP for data management.

Step 3: Gain experience with real-world projects

Once you’ve established a foundation in data engineering, it’s time to put your knowledge into action with real-world practical experience. Solidifying your skills in areas like building a data pipeline or implementing ETL processes will enable you to create a portfolio to help you secure future jobs.

Start with a beginner project like building a basic pipeline, before moving on to analysing sensor data or developing a cloud-based data warehouse. Eventually, you’ll be able to tackle tasks like creating a machine learning pipeline or building a real-time analytics dashboard.

Step 4: Plan your data engineering job search strategy

Searching for a job in data engineering should be a multi-pronged strategy, which uses job portals, CV search features and networking. These elements should be backed up with an optimised CV and a portfolio that showcases the projects you’ve worked on to bring the theory into the real world.