How To Become a Data Engineer: Salary & Career Paths for 2025

Co-Founder and Chief Data Scientist

Many aspiring students and professionals seeking career advancement often struggle to understand the complex path toward becoming a data engineer. As companies across industries rely increasingly on data-driven decision-making, the demand for skilled data engineers continues to surge. The demand for data engineers is growing rapidly, with a 21% job growth projection from 2018 to 2028, yet many still wonder how to make a successful transition into this high-growth career.

This article, prepared by experts in career planning, will provide clear guidance on how to become a data engineer. We will explore the education requirements, various career paths, potential salary ranges, and the long-term outlook for the field, helping you build a clear and actionable plan for your data engineering career.

Key Things You Should Know About Becoming a Data Engineer

Projected Job Outlook – The job outlook for data engineers is strong, with a projected growth rate of 21% from 2018 to 2028, driven by increased reliance on data for decision-making.
Career Advancement Opportunities – Data engineers can advance into specialized roles such as machine learning engineers, ETL developers, or data science managers, with opportunities to lead teams and work on more complex projects.
Education Requirements – Most data engineers hold a bachelor's degree in fields like computer science, electrical engineering, or business, with many pursuing advanced degrees or certifications to enhance their career prospects.
Diverse Work Setting – Data engineers work across a wide range of industries, including technology, finance, healthcare, and energy, with opportunities for remote or hybrid work options.
Salary Range – Data engineers earn an average salary of $109,675 per year, with a salary range typically between $80,000 and $149,000 annually.

What is a data engineer?
How to become a data engineer for 2025?
What does a data engineer do?
How much can you earn as a data engineer?
What are the career paths for data engineers?
What are the industries that hire data engineers?
What are the essential skills required to be a data engineer?
Can an accelerated online degree boost your data engineering career?
What interdisciplinary resources can further enhance a data engineer’s expertise?
What challenges do data engineers face in today's rapidly evolving technological landscape?
Should data engineers pursue advanced degrees to stay competitive?
How do you choose the right educational program for data engineering?
What emerging trends should data engineers watch for?
What certifications and training programs are available for data engineers?
What are the costs associated with becoming a data engineer?
What is the job outlook for data engineers?
Other Things You Should Know About Becoming a Data Engineer

What is a data engineer?

A data engineer is a professional who specializes in designing, constructing, and maintaining systems that enable organizations to collect, store, and analyze data efficiently. These systems are foundational for businesses to transform raw data into actionable insights, facilitating informed decision-making and strategic planning. Careers in data engineering typically require a background in computer science or IT, along with skills in programming, data modeling, ETL processes, and big data technologies.

The big data and data engineering services market is projected to grow at a CAGR of approximately 9.5% by 2033, driving a rising demand for skilled professionals and creating significant career opportunities. As the volume of global data continues to increase, the need for advanced data processing and analysis intensifies. Additionally, businesses across various industries are investing in digital technologies, further boosting the demand for data engineering services to facilitate these transformations.

How to become a data engineer for 2025?

Becoming a data engineer involves obtaining relevant education, gaining hands-on experience, earning certifications, and applying for positions in the field. Here are the key steps to pursue a career in data engineering.

Step 1: Obtain Relevant Education

Obtaining relevant education is the initial step to becoming a data engineer. Most entry-level positions require a bachelor's degree in a related field. Advanced roles may require a master’s degree or specialized training, depending on the complexity of the job and the employer’s requirements.

Based on industry data, around 65% of data engineers hold a bachelor’s degree, while 22% earn a master’s degree. Additionally, some institutions provide the best associate degree in 6 months online in data engineering or related fields, offering a faster path to enter the industry.

Most data engineers major in computer science. However, other related disciplines also provide a good pathway into the field. Common majors among data engineers include:

Computer Science (25%)
Electrical Engineering (14%)
Business (8%)
Computer Engineering (8%)
Other Majors (45%)

Step 2: Gain Hands-On Experience

Practical experience allows you to apply theoretical knowledge, solve real problems, and become familiar with industry-standard tools and practices. Internships, entry-level tech jobs, or project-based learning are great ways to build this experience. Working with databases, writing SQL queries, and building data pipelines can strengthen your skills and make you more competitive in the job market.

Step 3: Earn Certifications

Earning certifications validates your skills and knowledge as a data engineer. Choose certifications that focus on core areas like database management, cloud technologies, and data pipeline development. Ensure the certification is recognized in the industry and aligns with the tools or platforms you want to specialize in.

Step 4: Network and Apply

Once you've gained the necessary education and hands-on experience, connect with professionals in the field and explore job opportunities. Networking and applying help you stay updated on industry trends, build professional relationships, and increase your visibility in the field. Here are considerations to help you in this process:

Build a Strong Online Presence: Highlight your skills, projects, and certifications on platforms to attract recruiters and connect with professionals.
Attend Industry Events: Conferences, meetups, and webinars connect you with professionals and expose you to new tools and trends.
Tailor Your Resume: Focus on relevant skills, projects, and tools that match the job description.
Prepare for Interviews: Practice answering technical questions and explaining your experience with data systems.
Apply Consistently: Regular applications increase your chances of landing interviews and finding the right fit.

What does a data engineer do?

A data engineer performs a range of duties to provide robust data systems that collect, store, and process large datasets, making them accessible and useful for analysis. Here are the key responsibilities:

Developing Data Pipelines: Designing and managing ETL processes to efficiently move data from various sources to storage systems.
Building Data Infrastructure: Creating and maintaining databases and data warehouses to ensure scalable and secure data storage solutions.
Ensuring Data Quality: Implementing data validation and quality checks to maintain accuracy and consistency across datasets.
Collaborating with Teams: Working closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet business needs.
Optimizing Data Systems: Enhancing the performance of data systems by optimizing queries and improving processing speeds.
Data Modeling and Architecture: Designing and maintaining data models, schemas, and database structures to support analytical and operational needs.
Implementing Data Security Measures: Ensuring that data storage and processing comply with security protocols and regulations to protect sensitive information.

How much can you earn as a data engineer?

As a data engineer, the average salary in the United States is around $109,675 per year, equating to approximately $52.73 per hour. Salaries typically range between $80,000 and $149,000 annually.

Entry-level data engineers typically earn around $87,210 annually, while more experienced professionals can see salaries well above $151,128, especially at the senior or lead levels. Education also impacts earnings, with advanced degrees or specialized certifications often leading to higher-paying opportunities.

Data engineers working in the energy, mining & utilities sector earn significantly more, with an average salary of $142,640 per year. Different states and cities offer varying salary levels due to factors like the cost of living, demand for tech talent, and industry concentration.

What are the career paths for data engineers?

Data engineers have various career paths they can pursue, each focusing on different aspects of data management, analysis, and infrastructure. Here are some common paths:

1. Machine Learning Engineer

A machine learning engineer designs and builds algorithms that allow computers to learn from data. They focus on implementing models and systems for predictive analytics, automating decision-making processes, and improving performance over time. Some may consider pursuing the easiest PhD program in machine learning to further enhance their expertise and career prospects in the field.

Median salary: $128,769 per year.

2. ETL Developer

An ETL developer specializes in extracting, transforming, and loading data. They ensure data from various sources is cleaned, formatted, and made available for analysis. Their work involves building robust pipelines, optimizing performance, and ensuring data integrity. ETL developers are essential for ensuring smooth data flow within organizations, supporting analytics and business intelligence.

Median salary: $119,346 per year.

3. Data Operations Engineer

A data operations engineer manages and optimizes data infrastructure and pipelines to ensure smooth data flow. They focus on automating and streamlining data processes, addressing data bottlenecks, and ensuring system reliability. DataOps professionals work closely with data engineers, scientists, and business teams to enable efficient data management and faster decision-making.

Median salary: $85,029 per year.

4. Data Science Manager

A data science manager oversees a team of data scientists and engineers. They lead the development of data-driven strategies and initiatives. In this role, you’ll manage projects related to data analysis, predictive modeling, and AI. Some institutions offer 1 year degrees that pay well, including data science programs that provide accelerated pathways for those looking to enter the field quickly with high earning potential.

Median salary: $97,145 per year.

5. Chief Data Officer

A chief data officer is responsible for overseeing an organization's data strategy. The role includes managing data governance, ensuring data quality, and creating policies to maximize data’s value. CDOs work closely with other executives to integrate data-driven decision-making into the company’s strategic goals and ensure data compliance with regulations and industry standards.

Median salary: $151,203 per year.

What are the industries that hire data engineers?

Data engineers are in high demand across multiple industries due to the growing reliance on data for decision-making and operations. Here’s a breakdown of the industries that typically hire data engineers:

Technology (24%): Tech companies, particularly those focused on software, cloud computing, and AI, hire a large number of data engineers to manage and optimize vast amounts of data.
Fortune 500 (18%): Large corporations in various sectors require data engineers to support their big data and analytics initiatives.
Finance (10%): Financial institutions, such as banks and investment firms, need data engineers to handle transactional data, risk management, and regulatory compliance.
Internet (7%): Online platforms, e-commerce businesses, and digital media companies employ data engineers to process user data and improve customer experiences.
Professional (7%): Consulting firms, law firms, and other professional services use data engineers to manage large datasets for business analysis, market trends, and more.
Start-ups (6%): Growing startups, especially those in tech and e-commerce, require data engineers to build and scale their data systems quickly.
Health Care (4%): Medical institutions and health tech companies rely on data engineers to process patient data. Additionally, accelerated MHA programs online bridge the gap between data engineering and healthcare management, allowing professionals to specialize in both fields.

What are the essential skills required to be a data engineer?

A data engineer needs a mix of technical and general skills to manage large datasets, build systems, and ensure smooth data flow. Developing these skills helps advance your career and enhance your ability to contribute to data-driven decision-making.

Technical Skills Required for Data Engineering

Data engineers must be proficient in various tools and technologies to manage and process data. Here are five essential technical skills:

Programming Languages: Proficiency in Python, Java, and SQL is essential. Python and Java help write data pipelines, while SQL is used for managing and querying databases.
Data Warehousing and Databases: Knowledge of data warehousing solutions like Amazon Redshift, Google BigQuery, and databases like MySQL is important for storing and retrieving data efficiently.
ETL Processes: Data engineers must design and manage ETL pipelines that extract, transform, and load data into usable formats for analysis and storage.
Big Data Technologies: Familiarity with big data frameworks such as Hadoop and Spark is essential for processing large datasets and ensuring scalability.
Cloud Platforms: Experience with cloud platforms like AWS, Azure, and Google Cloud is crucial for managing storage, computing, and infrastructure on a large scale.

General Skills Required for Data Engineering

In addition to technical knowledge, data engineers need certain general skills to work effectively in teams and manage complex tasks. Here are five important general skills:

Problem-Solving: Strong problem-solving skills are essential to address data-related challenges, such as performance issues or data inconsistencies.
Attention to Detail: A keen attention to detail is essential to ensure accuracy and avoid errors that can lead to incorrect analysis or system failures.
Communication Skills: Data engineers must be able to communicate technical concepts clearly to both technical and non-technical team members.
Time Management: Effective time management is necessary for meeting deadlines and managing multiple tasks in data engineering projects.
Collaboration and Teamwork: Data engineers must work well with others, sharing knowledge and contributing to the success of team projects.

In addition, developing specific data engineer skills helps you become more proficient in building and optimizing data systems. Skill distribution shows that 12.19% of a data engineer's focus is on Python, highlighting its importance in the field for managing complex data systems and workflows.

Can an accelerated online degree boost your data engineering career?

Most online accelerated degree programs are designed for students requiring a flexible and time-efficient education path that aligns with industry demands. These programs integrate cutting-edge technologies with core computer science principles, enabling faster acquisition of practical skills and real-world project experience. Pursuing a 2-year bachelor degree computer science can streamline the transition into data engineering by offering intensive coursework and strategic career support, including mentor guidance and industry collaborations.

What interdisciplinary resources can further enhance a data engineer’s expertise?

Data engineers benefit from expanding their competencies beyond core technical skills. Pursuing interdisciplinary courses—such as those focusing on design thinking, user experience, or innovative project management—can offer fresh insights and practical strategies that foster creative problem-solving. Professionals may find value in supplementing their training with courses from diverse fields to drive integrations between data infrastructure and emerging digital solutions. For example, exploring specialized programs, including online game design classes, provides an alternative perspective that can help refine data processes and enhance overall project outcomes.

What challenges do data engineers face in today's rapidly evolving technological landscape?

Data engineers must navigate ever-changing technology standards and complex data security regulations. Balancing the integration of new tools with legacy systems, they face the pressure to adopt cloud platforms and big data frameworks rapidly, while ensuring compliance and robust cybersecurity practices. Additionally, managing cross-functional collaboration in environments where stakeholders expect real-time insights can be demanding. Continuous learning and proactive adaptation are critical, as trends seen in related fields, such as the health informatics job outlook, underline the importance of staying ahead in a fast-paced digital landscape.

Should data engineers pursue advanced degrees to stay competitive?

Advanced education can provide data engineers with a strategic advantage by deepening technical knowledge and fostering interdisciplinary innovation. Pursuing a master’s degree or specialized training can enhance leadership capabilities, improve complex problem-solving, and expand understanding of emerging technologies that drive digital transformation. Professionals may consider programs that integrate data science, cloud computing, and business analytics to build a more holistic skillset. For those exploring further academic pathways, research options such as the cheapest masters in artificial intelligence to balance cost with advanced learning opportunities.

How do you choose the right educational program for data engineering?

When evaluating educational programs for data engineering, focus on curriculum depth, instructional quality, and career support services. Assess whether the program emphasizes practical projects, partnerships with industry, and access to modern tools and technologies. Consider factors such as accreditation, program flexibility, and alumni success. Compare options to ensure they align with your career goals and skill development needs, including exploring options like affordable online data science masters programs for cost-effective and comprehensive learning solutions.

What emerging trends should data engineers watch for?

Data engineering is evolving with trends such as edge computing, real-time analytics, and the convergence of artificial intelligence and data management. These innovations are driving shifts in infrastructure, with hybrid cloud solutions and automation redefining data workflows. Professionals are encouraged to monitor these advancements to identify specialized niches and prepare for future challenges. Additionally, individuals transitioning from adjacent fields can benefit from expanding their interdisciplinary expertise—exploring programs like the cheap nursing informatics online program may provide valuable insights into data practices within healthcare.

What certifications and training programs are available for data engineers?

Several certifications and training programs can help data engineers build expertise and enhance their careers. Here are the notable options:

AWS Certified Data Engineer – Associate: This certification from Amazon Web Services (AWS) demonstrates expertise in using AWS services to manage data pipelines, data lakes, and machine learning models.
Google Cloud Professional Data Engineer: Offered by Google Cloud, this certification validates skills in designing, building, and managing data processing systems on Google Cloud.
IBM Data Engineering Professional Certificate: This program covers building data pipelines, managing databases, and working with tools like Apache Airflow and Kafka, helping learners become job-ready.
Databricks Data Engineer Associate Certification: Databricks offers a certification that focuses on using their platform to build and maintain data pipelines, crucial for managing large datasets.
Microsoft Certified: Azure Data Engineer Associate: This certification assesses knowledge in integrating, transforming, and consolidating data on Microsoft Azure, with a focus on data solutions and analytics.
Data Engineering Nanodegree by Udacity: Udacity’s program teaches data modeling, pipeline automation, and working with big data tools, providing hands-on experience.
DataCamp: Offers beginner to advanced training courses in data engineering, focusing on various tools and techniques like SQL, Python, and cloud technologies.

What are the costs associated with becoming a data engineer?

Tuition fees for relevant programs vary widely based on factors such as institution type, program level, and residency status. A bachelor’s degree in computer science, information technology, or a related field typically ranges from $10,000 to $50,000 per year. Graduate degrees can cost anywhere from $20,000 to $60,000 or more per year. Many cheap distance learning degree programs are available that can help reduce the overall cost of obtaining the necessary qualifications for a career in data engineering.

Additional costs may include certifications in relevant technologies, which often range from $100 to $500 per exam. Online boot camps, which offer more affordable training, can cost between $5,000 to $20,000 for a few months of instruction.

Financial aid can significantly reduce the cost of becoming a data engineer. Most accredited colleges and universities offer several types of aid for eligible students pursuing degrees in computer science or data-related fields. These include:

Federal Student Aid: In the U.S., students can apply for federal grants, loans, and work-study programs by completing the Free Application for Federal Student Aid (FAFSA).
State and Institutional Aid: Public universities and state programs may offer grants or scholarships to in-state residents.
Assistantships: Graduate teaching or research assistant positions can offer stipends and tuition remission.
Employer Sponsorships: Some employers offer tuition assistance or reimbursement for relevant courses and certifications.

What is the job outlook for data engineers?

The job outlook for data engineers is promising, with a projected growth rate of 21% from 2018 to 2028, much faster than the average for all occupations. Over the next decade, around 284,100 new data engineer jobs are expected to open up as organizations continue to rely on data-driven decision-making. The demand for skilled professionals in this field is increasing as businesses gather and analyze large volumes of data to drive their operations.

In addition to job growth, the demand for data engineers has also positively impacted salaries. Over the past five years, data engineer salaries have increased by 10%. Currently, there are more than 10,573 data engineers employed in the United States, and the demand continues to rise, with 303,105 active job openings in the country. With strong job prospects and competitive salaries, data engineering remains a promising career path.

Here’s What Graduates Have to Say About Becoming a Data Engineer

Pursuing a degree in data engineering was definitely challenging. There were moments when I thought I wouldn’t make it, but the skills I’ve gained have been invaluable. The work is demanding, but the flexibility to work from home and set my own hours has made it all worth it. – Nathaniel

The flexibility of being a data engineer is a huge perk. I have the option to work remotely or adjust my schedule, which helps me maintain a healthy work-life balance. It’s great to know that I can still make a big impact while having control over my time. – Joyce

Getting my degree in data engineering was a challenge, but the real learning began when I started working. I now enjoy the flexibility of remote work and the satisfaction of building data systems that solve real problems. It’s rewarding to see the impact of my work on the company’s success. – Aldrich

Key Findings

The education cost for a data engineer ranges from $10,000 to $50,000 per year for a bachelor's degree, with graduate degrees costing $20,000 to $60,000.
Approximately 65% of data engineers hold a bachelor's degree, while 22% earn a master’s degree.
Data engineers in the technology sector make up 24% of the workforce, with other prominent industries including finance (10%) and Fortune 500 companies (18%).
Common majors among data engineers include computer science (25%), electrical engineering (14%), business (8%), computer engineering (8%), and other related majors (45%).
Over 303,105 active job openings for data engineers in the U.S. as of the latest data, with 284,100 new positions expected to open up by 2028.

Other Things You Should Know About Becoming a Data Engineer

How long does it take to become a data engineer?

Becoming a data engineer typically takes 4 to 6 years. This includes earning a bachelor's degree in computer science or a related field, which usually takes around 4 years. Many data engineers also pursue certifications or advanced degrees, which may take an additional 1 to 2 years. Gaining hands-on experience through internships or entry-level jobs is also key to developing the necessary skills for the role.

Can you become a data engineer with a computer science degree?

Yes, you can become a data engineer with a computer science degree. A computer science degree provides a strong foundation in programming, algorithms, and databases, which are essential skills for data engineering. However, you may need additional training or experience with data management tools, big data technologies, and cloud platforms to specialize in data engineering.

What’s the difference between a data engineer and a data architect?

A data engineer builds and maintains systems that process and store data, while a data architect designs the structure of these systems. Data engineers focus on creating pipelines, ensuring data flows smoothly, and working with databases. Data architects, on the other hand, define the blueprint for how data should be organized and ensure that systems are scalable and secure to meet long-term business needs.

What programming languages should a data engineer learn?

A data engineer should learn languages like Python, SQL, and Java. Python is commonly used for data manipulation, while SQL is essential for querying databases. Java is often used in big data tools like Hadoop and Spark. Additionally, learning languages like Scala or Go can be beneficial for handling large-scale data processing and working with cloud-based systems.

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