Data Engineering Career Path: Climbing to Success

The author of this article is tech expert Pieter Murphy.

Data engineering is designing and building systems that collect, store, and analyze data at scale. It is a very broad field that has applications in almost every industry.

Organizations and companies can collect massive amounts of it, and they, therefore, need the right technology and people to ensure that it is usable by the time it reaches analysts and scientists.

On top of making the work of other professionals in the field easier, taking the data engineer career path has an assured future, where 463 exabytes will be produced every day by 2025. The future of most industries looking to be competitive on the world stage is building toward a reliance on deep learning and machine learning, which depends heavily on engineers to channel and process the information gathered from multiple sources.

In this article, we'll explore the intricacies of becoming a data engineer, delineate the key skills and tools you must master, delve into the educational prerequisites, and outline the progressive trajectory of this career.

Additionally, we'll peer into the future of the path to become a data engineer, discuss the importance of community engagement, and offer insights on securing that all-important first job in the field.

The Role and Responsibilities of a Data Engineer


The role of an engineer varies depending on the requirements of the projects. Their role directly correlates to the overall complexity of infrastructure.

However, one thing is clear when you look at the needs of the field’s science arm in general – the more cutting-edge tech, such as artificial intelligence and machine learning, are involved, the more resource-heavy and complex pipelines get.

In your career as a data engineer, your responsibilities depend on your role, which falls into the following broad categories:

General role

The engineer role is part of a small team comprising other professionals. They are responsible for every flow step, from source configuration to the integration of analytical tools. A general-role engineer architect builds and manages all these systems.


Warehouses today are much more diverse than they were in the past. Because of this, multiple engineers are needed, some focusing solely on architecting a warehouse. Warehouse-centric engineers may also have to deal with different big data tools like Kafaka and Hadoop, different types of storage such as SQL and NoSQL, and integration tools. We will get into this in a little while.


These engineers use integration tools that connect a warehouse to sources. These tools can carry on specific tasks or just load information from one place to another. For instance, they may have areas for staging data, where it arrives before it is transformed. A pipeline-centric engineer would focus on managing this layer of the ecosystem.


As an aspiring engineer, you will be the specialist who designs, optimizes, and manages infrastructure and builds pipelines based on business requirements. Your role can correspond to each of its parts individually or to the whole system simultaneously.

Your data engineering career path involves understanding the three important phases that it undergoes before consumers and business users see it in the forms of readable reports or dashboards:

1. Extraction

Data constantly surrounds us, but we first must extract it from its sources to make sense of it. It can come from IoT sensors scattered, for example, across a manufacturing facility or an aircraft. Public sources available online are also another source of information. Your data engineering career path will start with knowing how to get it.

2. Transformation

Raw data makes very little sense to end users because it is difficult to analyze initially. The transformation phase involves cleaning, formatting, and structuring sets to make them consumable for reporting and analysis.

As part of your career in data engineering, you will learn how to prepare it for use through transformation.

3. Loading and storage

The extracted information needs to be kept somewhere. Databases and warehouses are the most common forms, but storage may exist in different forms, including lakehouses, lakes, marts, etc.

The processes named above in that particular order form Extract, Transform, Load — or the ETL pipeline, the most common pipeline.

etl pipeline in data engineering

To climb the data engineer career ladder, you need a combination of skills and knowledge in engineering, computer science, and databases. Despite the specific area of focus, engineers have similar responsibilities. They include:

  • Architecture design: Designing the architecture of a platform is at the core of engineering. However, big companies with complex processes might have a separate role for an architect.
  • Developing data-related instruments: Engineering is, first of all, a developer role. As such, engineers use their programming skills to manage, customize, and build integration tools, warehouses, analytical systems, and more.
  • Pipeline testing/maintenance: Engineers test the performance and reliability of each part of the system during the development stage. g -Alternatively, they can choose to cooperate with the testing team.
  • Machine learning algorithm deployment: Under this role, the scientists design machine learning models, while engineers are responsible for deploying those models into production environments. The deployment involves hooking up the model to sources, managing computing resources, configuring attributes, setting up monitoring tools, etc. But again, some companies may assign this role to machine learning engineers.
  • Manage metadata: Engineers are responsible for managing metadata, where no separate roles exist. This information can tell you the origin, meaning, ownership, lineage, creation, version and schema of your data.
  • Provide access tools: You can work in different industries providing access tools. For instance, you can see data engineer career growth as a Business Intelligence engineer, setting up tools to generate reports and create visuals.
  • Track pipeline stability: The warehouse needs to be cleaned from time to time; therefore, it is important to monitor the stability and overall performance of the system. The automated parts of the pipeline also need to be modified and monitored because approaches and requirements may change.

Key Skills and Tools for a Data Engineer

Given that an engineer can perform various tasks, you can tailor your approach for every organization or project's required skill set. The career path of a data engineer will see them master the following:

Engineering skills

You must have a software engineering background to see success in your data engineer pathway. You must be proficient in one or more programming languages. Most tools and systems for big data or analysis are written in Scala and Java.

Most engineers master Python first because it is the most used language for machine learning projects due to its rising popularity. C/C# and Golang are high-performant languages popular among engineers, especially for implementing and training ML models.

Warehouse/database knowledge

As we have already established, engineers usually deal with different types of storage like NoSQL and SQL databases. As such, you must know how to handle them and familiarize yourself with several management systems, including Oracle, MySQL, MongoDB, PostgreSQL, and others.

A warehouse is the main component of most pipelines, so you will need knowledge of modern cloud-based warehouse technologies like Snowflake, BigQuery, Firebolt, and Amazon Redshift to choose the best warehouse architecture, implement it, and maintain it properly.

Data-related expertise

As an engineer, you will work closely with scientists. You, therefore, need a strong understanding of algorithms, modeling, and transformation techniques, as they are the basics of working with infrastructures.

Your data engineer career progression will require you to gain expertise in:

  • Analysis;
  • Science concepts;
  • Mastery of ETL/ELT tools (IBM DataStage Informatica Power Center, Oracle Data Integrator, Talend Open Studio);
  • ML frameworks and libraries knowledge (TensorFlow, Spark, PyTorch, mlpack);
  • Big data technologies (Hadoop, Kafka); and
  • BI tools knowledge (Tableau, Microsoft Power BI).

Data Engineering Educational Path

Educational Background Required for an Engineer

While some engineers start with a bachelor's degree in computer science, IT, or a related field, the path to a data engineer career sees others transition from roles such as software engineering or business intelligence analysis.

Advanced degrees, such as a master's in data engineering or science, provide a deeper understanding of the field. Pursuing a master's degree can also be beneficial for career advancement.

You can also take the self-taught route, where opportunities for formal education are not accessible, but routes to advance your data engineer skills abound. To learn more about that, check out our blog on the difference between self-taught and degree programmers to learn what each path looks like.

Relevant Subjects and Areas of Study

The relevant subjects and areas of study for an engineering career include:

  • database management
  • structures
  • algorithms
  • coding
  • mining
  • security
  • operating systems
  • distributed computing… and more

areas of study for a data engineer

These form the bedrock of a data engineering pathway during the education phase. Specializations in any of these areas can offer a competitive advantage as the world becomes overloaded with sensors and needs more engineers to make sense of it all.

Technical Skills for a Data Engineer

Beyond the foundational skills, your career goals for a data engineer job will see you develop/master the following technical skills:

  1. Understanding Warehousing Principles: Essential for designing and managing large-scale repositories.
  2. Ensuring Security: A critical aspect of an engineer's role is safeguarding the privacy and security of what is collected.
  3. Version Control Systems: Mastery of version control systems is important for tracking changes and maintaining visibility throughout processes.

Soft Skills for a Data Engineer

You need several soft skills to augment your technical mastery of the field. The most significant ones are problem-solving and creativity.

Soft skills are a core part of your data engineer career goals and include being highly attentive to detail, having the patience to carry out monotonous tasks, and breaking down big problems into smaller, more achievable tasks.

You also need great communication skills to successfully collaborate with colleagues and other stakeholders.

Data Engineer Career Progression

So, what is the data engineer career outlook today?

Career aspirations for data engineers will see them follow a well-defined path, moving through various stages that reflect increasing expertise, responsibility, and leadership levels. Let's navigate through the stages of your potential career journey.


The Trainee phase marks the entry into the engineering career track. Individuals at this stage are typically recent graduates or those transitioning from related fields.

As Trainees, individuals focus on building a foundational understanding of engineering principles, tools, and practices.

Roles and Responsibilities:

  • Trainees engage in hands-on learning, familiarizing themselves with fundamental concepts such as modeling, ETL processes, and database management.
  • Working on smaller projects, Trainees apply their knowledge in real-world scenarios under the guidance of more experienced team members.
  • Trainees become acquainted with the tools and technologies commonly used in data engineering, gaining proficiency in languages like Python or Java.

Junior Data Engineer

A junior engineer is an entry-level professional with a bachelor’s degree in computer science or a similar subject. After successfully navigating the Trainee phase, individuals move to this step where they get warehousing, database management, and software development skills.

Roles and Responsibilities:

  • Junior engineers are supervised and work under senior engineers.
  • Certification and continuous training in emerging and advanced technologies can accelerate career progression.
  • Junior engineers help to complete quality checks, integrate data from various sources, and collaborate with experienced engineers to develop and design databases and pipelines.

Middle Data Engineer

Warehouse Engineer is an example of a job title for a mid-level engineer. Middle-level engineers are professionals who deeply understand the field’s concepts and how to apply them.

Roles and Responsibilities:

  • Mid-level engineers oversee ETL operations, manage and design infrastructure, ensure quality and security, and improve models.
  • They often work with other experts in the field, like scientists and analysts, to complement their needs.

Senior Data Engineer

Senior Engineers have established professionals who have surpassed the middle-level stage and thoroughly comprehend engineering concepts. You can work as the Lead or Principal Data Engineer at this level.

Roles and Responsibilities:

  • Senior engineers mentor and lead teams of engineers, manage big data platforms and build and implement complex architectures.
  • They monitor the system’s performance and scalability and are responsible for its security.
  • They may collaborate with business stakeholders and relevant professionals to develop and deploy objective solutions.

Data Science Engineer

A data science engineer designs, builds and executes data-driven systems.

Roles and Responsibilities:

  • They collaborate with analysts and other stakeholders to create and deploy machine learning models and data pipelines.

Data Architect

The architects design and maintain architectures to support the organization’s objectives.

Roles and Responsibilities:

  • They architects ensure the architecture’s efficiency, scalability, and security.
  • They also ensure compliance with privacy laws and industry regulations.

Manager of Data Engineering

They are a senior-level professional who leads a team of engineers to ensure the organization’s infrastructure is efficient, scalable and secure.

Roles and Responsibilities:

  • Their primary role is overseeing pipeline design, development, and deployment.
  • They also participate in data governance.

Chief Data Officer (CDO)

A CDO is a C-level executive responsible for governing, managing, and using an organization’s data assets. This is where the data engineering path reaches the pinnacle of specialization.

Roles and Responsibilities:

  • CDOs lead strategic use to improve operational performance, promote business growth, and reduce risks.
  • They implement protocols, rules, and standards for collecting, analyzing, storing, and sharing an organization’s data.
  • They directly work with company management to develop new products, identify insights-driven opportunities, improve customer sales, and increase sales.

The Future of Data Engineering

As we gaze into the future of engineering, it becomes evident that the field is continually evolving. Integrating machine learning, artificial intelligence, and automation into the engineering processes is set to redefine the landscape.

The data engineer's future career path will ask professionals to be proficient in handling vast datasets, navigating the complexities of emerging technologies, and leveraging them to derive actionable insights.

Diving into a Data Engineering Community

You should join and be a part of an engineering community, as it is invaluable for staying abreast of industry trends, networking with professionals, and accessing resources that facilitate continuous learning and growth.

How to Find a First Job in Data Engineering

The eventual goal in the career path for a data engineer is to secure employment with opportunities for growth and great pay. When you feel qualified enough to meet potential employers’ standards, here’s how you find your first job:

  • Internships: Seek internships to gain practical experience and exposure to real-world engineering challenges.
  • Networking Events: Attend industry conferences, meetups, and networking events to connect with professionals and potential employers.
  • Online Job Platforms: Utilize online platforms to explore job opportunities, emphasizing platforms tailored to tech and data-related roles.
  • Build an Online Presence: Showcase your skills and projects through online portfolios and platforms like GitHub to attract potential employers.

Passionate About Data? Consider Designing and Building the Systems Used

In conclusion, the data engineering path offers a dynamic and rewarding path for those passionate about working within the field. Mastery of technical skills, effective communication, and collaboration positions individuals for success in this evolving field.

As the engineering landscape transforms, those embarking on this exciting career path can anticipate a journey marked by continuous learning, innovation, and professional fulfillment.

The views expressed in the articles on this site are solely those of the authors and do not necessarily reflect the opinions or views of Anywhere Club or its members.
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