The Data Engineer Job Market in 2024 [Research on 1,000 Job Postings]

With the emergence of ChatGPT, recent years have seen concerns about how artificial intelligence (AI) could lead to job losses in data-related fields like data science and data engineering. There was speculation that AI could replace these roles because of its capacity to automate tasks traditionally performed by humans.

But how is this changing landscape affecting the data engineer job market, and where do data engineers fit within the wider data ecosystem?

Contrary to common concerns, data and predictions from the U.S. Bureau of Labor Statistics show little indication of an increased rate of job losses or a significant shift from previous trends due to the AI revolution. In fact, the data suggests that data-related sectors are currently among the fastest-growing.

As AI continues to transform the tech industry, the role of data engineers becomes increasingly salient. These professionals are responsible for designing, building, and maintaining the data infrastructures that AI systems depend on.

To better understand this year’s data engineering job market, we've examined 1,000 job postings to reveal the current data engineering job trends and identify the skills employers are seeking.

This article explores the rapid job growth of data engineering and the emerging opportunities in this evolving field.

Key Findings

• 10.1% of the companies declared their location as "Remote."
• Just over a quarter of job postings (26.17%) openly disclose salary ranges.
• While data engineering skills remain crucial for data engineers, many employers seek a broader knowledge base, including advanced skills from data architecture and data science fields.
• Understanding Microsoft Azure is essential, with 74.5% of job listings specifying it as a necessary skill.
• SQL and Python remain the leading programming languages, mentioned in 79.4% and 73.7% of the job postings, respectively.
• About one-third of the job listings required a degree in fields less traditionally related to the position—including statistics, machine learning, physics, economics, and AI.
• 22.8% of postings don’t specify a degree level, suggesting that individuals with less traditional education may still be competitive.
• According to Glassdoor, the average annual salary of a data engineer in the US for 2024 is \$153,000.

Table of Contents

• Methodology
• What Does a Data Engineer Do?
• How Can You Become a Data Engineer?
• What Skills Do You Need to Become a Data Engineer?
• Where Do Data Engineers Work?
• How Much Do Data Engineers Make?
• What Is the Data Engineer Job Outlook in 2024?
• Starting a Data Engineering Career
• FAQs

Methodology

This comprehensive analysis explores 1,000 job postings for data engineer roles sourced from Indeed.

First, we gathered job listings from Indeed, ensuring various roles across different industries and locations. Then, we identified specific keywords related to data engineer roles directly from the job descriptions.

Through meticulous data preprocessing, we confirmed the accuracy and relevance of the dataset, ultimately reducing it to 703 relevant postings. The graphs and tables seen throughout this article refer to these relevant postings.

We aim to pinpoint critical trends, qualifications, and features of the current data engineer job market—focusing solely on opportunities within the US.

What Does a Data Engineer Do?

With the myriad of terminology inundating the data industry, you might ask yourself, “What exactly is a data engineer?”

Data engineering is an essential part of the tech industry, dedicated to designing, building, and maintaining infrastructure for large-scale data processing and analysis.

It involves creating robust infrastructures that facilitate efficient data collection, storage, processing, and access—essentially constructing the backbone of the data world.

Here’s an overview of a data engineer job description:

• Establish and maintain data architecture for acquisition, storage, and access.
• Develop and oversee databases and large-scale processing systems.
• Construct systems for data collection from diverse sources and automate storage.
• Transform raw data into usable formats for data scientists and analysts.
• Work with data scientists and IT teams to integrate data systems.

Data engineers form the cornerstone of an organization's data landscape, crafting infrastructure that converts raw data into actionable insights while guaranteeing accuracy, accessibility, and security. Their absence would impede organizations from leveraging their data effectively, rendering the data engineer role crucial in today's data-driven world.

How Can You Become a Data Engineer?

Data engineering is a specialized role requiring a unique combination of computer science, database management, and software engineering skills.

A typical data engineer career path requires an educational background in data engineering or computer science. The tech industry, however, values skills and abilities, implying that there are multiple routes to meet job qualifications.

As you might expect, the most sought-after degrees for data engineering roles are data engineering and computer science, followed by general engineering and data science.

Surprisingly, one-third of the job postings sought degrees in fields not traditionally associated with the role, such as statistics, machine learning, physics, economics, and AI.

Over 10% of job listings preferred candidates with an architecture degree, highlighting the parallels between architecture and data engineering. Both fields demand design and structural expertise for developing scalable, secure data infrastructures. An architectural background fosters systematic thinking and problem-solving abilities, which are vital for success in data engineering.

You don't necessarily need a computer science or data engineering degree to break into the field. Even if your degree is in a less related or completely different field, you still have an opportunity to secure your dream position if you learn the required skills through online courses, bootcamps, or other non-traditional means of education.

But what's the typical education requirement for most data engineer roles?

The above graph illustrates mixed results: nearly 40% of job listings seek candidates with a bachelor's degree, 34% prefer a master's, and a mere 4% demand a PhD.

One striking result, however, is that approximately 23% of the job postings don’t specify a degree requirement. While this doesn't necessarily mean these employers aren't looking for degree holders, it suggests that individuals without a traditional education may still be competitive candidates.

The tech industry highly prioritizes skills and capabilities. So, with a passion for your work, structured learning habits, and a substantial portfolio, you can excel in the data engineer job market, irrespective of your formal education.

The 365 data science platform is designed to equip you with a broad spectrum of skills necessary for a successful career in data engineering. We also present opportunities for hands-on projects, enabling you to demonstrate your competencies to potential employers. No matter your current level of expertise, our platform provides a place for you to develop the skills that the industry values.

What Technical Skills Do You Need as a Data Engineer?

Data engineers need a robust set of technical skills to construct reliable, scalable, and efficient data pipelines—ensuring that data is readily available for tasks like data analysis, business intelligence, and machine learning applications.

Our research results highlight the industry's shift towards more agile, scalable, and real-time data processing capabilities. This shift aligns with the need for immediate data-driven decision-making—leading to a demand for new data engineering technologies that support data containerization and orchestration.

Data Engineering Skills

Similarly, the broad scope of responsibilities within the role translates to various data engineering requirements, which can be grouped into the following categories.

Database Management

Knowledge of SQL and NoSQL databases is vital.

SQL databases like MySQL and PostgreSQL are crucial for structured data querying and management—appearing in almost 80% of job postings.

NoSQL databases like MongoDB and Cassandra are essential for handling unstructured or semi-structured data—appearing in 12.1% of job postings.

Big Data Technologies

Big data is cited in about 5% of job postings.

Specific data engineering technologies like Spark (41.1%), Hadoop (17.8%), and Hive (8.4%) are frequently mentioned.

Hadoop provides a framework for distributed storage and processing, while Spark offers real-time processing capabilities essential for handling big data efficiently.

Data Warehousing Solutions

Understanding data warehousing solutions like Amazon Redshift (6.1%), Google BigQuery (3.3%), or Snowflake (11.7%) is essential for data storage, analysis, and retrieval. These solutions enable engineers to manage and query large datasets effectively.

Data lakes—mentioned in about 25% of the postings—are non-traditional data warehouses that provide a more flexible environment for data exploration, analysis, and discovery.

ETL Processes

Skills in developing ETL processes—appearing in 57% of job postings—are crucial for data integration (4.1%), consolidation, and analytics.

This involves extracting data from various sources, transforming it into a usable format, and loading it into a target database or data warehouse.

Cloud Services

Knowledge of cloud platforms like Azure—mentioned in almost 75% of postings—is an increasingly essential skill for data engineers, enabling scalable and flexible data infrastructure.

Cloud platforms offer various services for data engineering, including data storage, processing, and analytics.

Data Pipeline and Workflow Management

Approximately 15% of job postings mention data pipeline management.

Experience with tools like Apache Kafka (16.2%) and Apache Airflow (3.3%) is essential for managing data pipelines and workflows.

These tools automate and schedule data processing tasks—ensuring data flow is consistent, reliable, and efficient.

Data Governance

Data governance is mentioned in 5% of postings.

This involves managing data's availability, usability, integrity, and security in an organization—critical skills for data engineers today.

Containerization

With the rise of microservices and cloud-native development, containerization technologies like Docker (9%) have become vital.

They allow data engineers to create, deploy, and manage applications in a flexible, scalable, and efficient manner.

Data Architecture Skills

Data engineering and architecture are closely related but distinct disciplines within the data management spectrum. The key difference lies in their primary focus.

Data architects design and structure data systems to meet business requirements and align with the data strategy. On the other hand, data engineers actively build and maintain the infrastructure for processing and analyzing data.

But the two roles often overlap. Data architecture follows a similar career path to data engineering, as seen in the following architectural skills required in the data engineer job postings.

One such skill is managing NoSQL databases—including MongoDB (1.0%)—and cloud computing platforms, which appear at 6.3% and 6.0%, respectively. This knowledge is essential for building scalable and flexible data systems—vital to the data engineering career.

Employers also look for microservices (3.3%) and distributed systems (3.0%) to ensure data operation reliability and efficiency, as well as RESTful APIs (1.0%) for integrating diverse data sources.

The overlapping skills reflect a general trend towards more multifaceted roles in the tech industry, as we also observed in our data science outlook research for 2024.

Programming Skills

Programming languages are critical technologies for data engineers to develop, manage, and enhance data systems and pipelines.

Critical programming languages mentioned in our data engineering job listings include the following.

SQL (79.4%): Essential for interacting with relational databases, querying data, and managing data stored in structured formats, SQL remains the most valued programming language for data engineers.

Python (73.7%): Due to its readability, comprehensive libraries, and flexibility in data manipulation, processing, and machine learning, Python remains a requirement in data engineering.

Java (22.6%) and Scala (17.5%): These languages remain essential due to their use in big data environments, particularly with such technologies as Apache Hadoop and Apache Spark.

R (14.9%): R is vital for statistical analysis and visualization in data engineering. While more common in data science, it's also used in data engineering to develop algorithms and process complex mathematical data.

JavaScript (3.4%): While not as central as Python or SQL, JavaScript can be a helpful skill in data engineering for developing front-end applications for data visualization, integrating with APIs, and handling data in web-based environments.

Cloud Skills

The demand for cloud skills among data engineers has increased as more organizations transition their data operations to the cloud. These skills enable data engineers to construct and manage scalable data infrastructures that efficiently handle diverse data volumes.

Cloud technologies also empower data engineers to enforce security measures and adhere to evolving data privacy regulations—a vital consideration in today's data-driven environments.

The following research results underscore the cloud's central role in modern data engineering.

• Microsoft Azure (74.5%), AWS (Amazon Web Services) (49.5%), and GCP (Google Cloud Platform) (21.3%) are the most in-demand cloud skills in the data engineering job market.
• Azure's prominence in 74.5% of job postings suggests that data engineering roles are heavily geared toward environments using Microsoft's cloud services.
• While Docker (9%) and Kubernetes (6.7%) are not cloud providers themselves, they are important for containerization and orchestration in cloud environments—reflecting a need for skills in managing and deploying applications.
• At 2.8%, Salesforce indicates a more niche demand for roles that interface with customer relationship management (CRM) data within the cloud.

While most data engineering job postings mention the need for specific cloud skills, some require certification.

The graph below displays the proportion of postings citing cloud certifications—illustrating the alignment of required certifications with the skills depicted in the above graph. Nonetheless, the table beneath this graph highlights that these figures are notably small.

Data Analysis Skills

While not the primary focus, data analysis is a crucial skill for a data engineer, improving their grasp of the data infrastructure's context and its impact on insights.

The bar chart shows that employers prioritize data mining principles (4.6%), SAS tools (3.3%), and statistical analysis skills (2.4%).

Data visualization (12.8%), however, is the most mentioned data analysis skill in the data engineer job postings because engineers need to manage data and communicate their findings effectively.

When looking at the specific data visualization tools mentioned in the job postings, Power BI leads appear in 22% of job postings, followed by Tableau at 19.3%. This underscores the importance of these tools for creating dashboards and reports. Looker also significantly features at 3.6%, indicating its growing role in data visualization.

Despite only being mentioned in 8.7% of data engineering job postings, proficiency in Microsoft Office tools is greatly valued by employers because it allows data engineers to share their work and insights in business environments effectively.

The above graph depicts the proportions of job postings that specifically ask for Microsoft Office skills. Excel leads with a 55.6% share—reflecting its crucial role in data manipulation and preliminary analysis. Employers also seek Word (18.5%) and PowerPoint (7.4%) skills for presenting findings.

AI & Machine Learning Skills

As AI continues to revolutionize every sector in the tech industry, its impact on data engineering is evident.

AI appears in 11% of job postings. In comparison, APIs (at 13.7%) underscore the significance of interfacing with AI services and integrating various machine learning models into systems—essential skills on the career path for data engineers.

Machine learning appears most frequently in 29.9% of postings. Although it has yet to become as essential to data engineering as it has to data science this year, it’s an increasingly desired skill due to its widespread use in automating tasks across various fields.

Machine learning enables data engineers to create data processing systems capable of learning from and adapting to data patterns. It automates tasks such as data cleaning and predictive analysis. This not only simplifies the workflow but also aids data scientists by preparing data to enhance the efficiency of machine learning models—an essential element of data engineers’ skills.

In 2024 data engineer job postings, optimization techniques are leading with 16.9%, reflecting their essential role in improving the efficiency of data systems and algorithms. Natural language processing (NLP) also stands out at 2.0%, showing demand for engineers who can manage and analyze textual data.

The relatively lower percentages for specific algorithms and methods like deep learning, clustering, and computer vision suggest that while these skills are essential, data engineers often focus more on the infrastructure supporting these techniques than their direct application. These skills are typically the domain of data scientists.

Where Do Data Engineers Work?

The 703 job postings for data engineers originated from 576 companies across 37 US states.

California leads in job offerings, with Virginia, Texas, Illinois, Massachusetts, New York, Washington, New Jersey, Florida, and Georgia in the top 10.

This year’s analysis shows that over 10% of job postings specify their location as remote. The rise of remote work provides engineers with a broader range of opportunities. It also allows companies to prioritize their employees' necessary skills over local availability, promoting greater team diversity.

But as location becomes less significant in the data engineer job market, this likely signals an increase in competition within the market because employers have access to worldwide talent.

How Much Do Data Engineers Make?

According to Glassdoor, the average salary of a data engineer in the US for 2024 is \$153,000 annually, ranging between \$120,000 and \$197,000. Our research supports this, with the most frequently mentioned salaries falling between \$120,000 and \$160,000. Interestingly, over 25% of the job postings openly listed the expected salary range.

But you may be curious about the average entry-level data engineer salary. Below are Glassdoor's estimates of the total annual pay for a data engineering role in 2024, broken down by experience level.

• 0–1 Years: \$91K–\$155K/yr
• 1–3 Years: \$102K–\$173K/yr
• 4–6 Years: \$112K–\$189K/yr
• 7–9 Years: \$114K–\$193K/yr
• 10–14 Years: \$117K–\$201K/yr
• 15+ Years: \$130K–\$225K/yr

Not all job postings specify a particular number of years of experience—only about 30% list a specific requirement.

Most postings that provided this information required 4 to 6 years of experience—accounting for about 11% of the total job postings—followed by the 2 to 4 years range at 9.6%.

The smallest group (2.3%) comprised entry-level positions requiring up to 2 years of experience—suggesting that most companies seek more experienced data engineers.

But don't lose hope. The fact that over two-thirds of the job postings did not mention any experience requirement could signify that the data engineer job market is open to fresh talent.

It's crucial to remember that this information is specifically related to the data engineer job outlook in the US. Various factors—such as location, company size, and industry sector—can influence salaries.

What Is the Data Engineer Job Outlook in 2024?

You might be wondering whether data engineers are in demand in 2024.

Data has become essential in decision-making processes, with each piece of information potentially providing valuable insights. The advent of big data has only amplified this importance.

As organizations rely more on data, its complexity and volume have increased—necessitating more sophisticated management and processing methods, leading to a heightened demand for data engineers.

Data engineers are critical for creating and maintaining the infrastructure that collects, stores, processes, and ensures data availability for analysis and decision-making. Demand is high for expertise in technologies like Apache Spark, Hadoop, and such cloud services as AWS.

According to the U.S. Bureau of Labor Statistics, employment for database administrators and architects is expected to grow by 8% from 2022 to 2032. This growth rate is above average for all occupations, making data engineering one of the year’s fastest-growing jobs this year.

The rising salaries of data engineers further underscore the value of these roles in the tech sector. As of January 2023, Glassdoor indicates that the average salary for a data engineer surged by \$40,000 to approximately \$113,000—marking a substantial annual growth.  Given these trends, the future of data engineering is promising.

Starting a Data Engineering Career

The data engineering job market is an ever-evolving landscape with a diverse set of skills and qualifications sought by employers. The data engineer career is becoming increasingly important as the backbone of an organization's data ecosystem. This is particularly noticeable with the rising demand for data-driven decision-making.

Essential skills like database management, big data technologies, cloud services, and programming languages—including SQL and Python—are vital in this industry. While many of these fall under the data engineering sphere, we also see advanced skills from other domains like data science and data architecture. This reflects the field’s interconnected nature and the growing demand for multi-faceted skills in the data engineer job market.

And even though traditional computer science or data engineering degrees are highly valued, the tech industry is progressively open to candidates with less traditionally related degrees—emphasizing the importance of skills and abilities over formal education.

The trend toward remote work is also noticeable, opening up a wider pool of opportunities for employers and potential employees. But this also means that candidates may face increased competition because they are vying for positions with a global pool of applicants.

The data engineer job market is particularly demanding of mid-level entry employees, which could raise the barrier to entry for newcomers. The career prospects for data engineers, however, remain promising in the job market right now, reflecting the growing recognition of their vital role in today's data-driven world.

Aspiring data engineers should strive to expand their skillset, stay updated with the latest technologies, and demonstrate their practical skills. Identifying the best resources is a good place to start.

The 365 platform provides courses that cover a wide range of skills—from database management using SQL to machine learning. We also offer projects where you can display your skills to future employers. No matter your starting point, a continued learning approach can help you stand out in the competitive data engineer job market.

FAQs