Data Engineering - Career Earnings Analysis (September 2025)

Colleagues, implementing a well-defined, forward-thinking career development plan can boost your career and income growth over a 30-year life cycle. According to Mordor Intelligence “The Big Data Engineering Services Market size is estimated at USD $91.54 billion in 2025, and is expected to reach USD $187.19 billion by 2030, at a CAGR of 15.38% during the forecast period (2025-2030).” By making modest investments in your professional training and certification will reward you with greatly enhanced income potential.

Assumptions:

• Duration: 30-year career lifecycle (e.g. age 25-55)

• Salary: $125,015/year - BuiltIn (compensation will vary by location - we will use a US average for our analysis)

• Education Level: This model is based upon the individual having a BS/BA degree. A MS/MA degree adds an extra 5%-10% to annual income

• Training & Certification: 5%-10% income lift/year

• Salary - Annual Increase per CPI Inflation: 2.5%/year

• Base Case: Junior Level - age 25/1st certification

• Intermediate Case: Senior Individual Contributor - age 30/2nd certification

• Advanced Case: Mid-Upper Management - age 35/3rd certification

• Expert Case: Technical Refresher - age 40-45/4th certification

Junior Level (5 years of experience):

• Title(s): Junior Data Engineer, Data Engineering Intern, Junior ETL Developer, Data Analyst

• Base income: $144,893/year

• Sample Training & Certs: IBM Data Engineering Professional Certificate, Databricks Certified Data Engineer Associate, Microsoft Certified: Azure Data Fundamentals (DP-900)

Intermediate (10 years of experience):

• Title(s): Data Engineer, Analytics Engineer, Cloud Data Engineer, Big Data Developer

• Base income: $175,408/year

• Sample Training & Certs: Google Cloud Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate (DP-203), AWS Certified Data Engineer - Associate, SnowPro Core Certification

Advanced (15 years of experience):

• Title(s): Senior Data Engineer, Data Architect, Principal Data Engineer, Data Engineering Manager

• Base income: $214,335/year

• Sample Training & Certs: Databricks Certified Data Engineer Professional, SnowPro Advanced: Data Engineer, AWS Certified Data Analytics – Specialty, Arcitura Certified Big Data Architect (BDSCP)

Expert (Executive-Refresher) (20 years of experience):

• Title(s): Director of Data Engineering/Data Science, Chief Data Officer (CDO), Principal Data Architect, VP of Data

• Base income: $350,000/year

• Sample Training & Certs: Certified Data Management Professional (CDMP), TOGAF 9 Certification, Specialized Vendor Certifications

Income Comparison:

• Base Case: Junior Level - $144,893/year

• Intermediate Case: Senior Individual Contributor - $175,408/year

• Advanced Case: Mid-Upper Management - $214,335/year

• Expert Case: Technical Refresher - $350,000/year

Note: For a more comprehensive roster of Data Engineering certifications see AWS, Databricks, Google, along with Coursera, edX, and Udacity.

Data Engineering Specializations, Master Classes and Certifications:

• Data Engineering, Big Data, and Machine Learning on GCP Specialization

• Data Engineering Foundations

• Data Engineering on AWS - Foundations

• Data Engineering with Microsoft Azure

• Data Engineering with AWS - Nanodegree Program

• DeepLearning.AI Data Engineering Professional Certificate

• IBM Data Engineering Professional Certificate

• Introduction to Data Engineering 

• Meta Database Engineer Professional Certificate

• Python Project for Data Engineering

• Snowflake Data Engineering Professional Certificate

Get started today (teams & execs are welcome).

Recommended Reading:

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

3 - The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age (Audible) (Kindle)

Much success in your Data Engineering career journey, Lawrence E. Wilson - Data Science Academy (share with colleagues & friends) 

Data Science - Discover the Top 3 Career & Earnings Growth Strategies

Colleagues, on average a Data Scientist earns $163k+ USD in the US according to Glassdoor. The first strategy is to Get Certified: A high quality cert from a reputable vendor or professional association can boost your income by 10%+.  Our top pick is the Google Data Analytics Professional Certificate with some 2.4m students enrolled in this Mag 7 program. Next, is the IBM Data Analyst Professional Certificate with over 328k students enrolled. And third, is Microsoft’s Power BI Data Analyst Professional Certificate (Exam PL-300) (228k enrollees). Second, Get Published: Write a series of posts or articles on Best Practices, Industry Trends and Emerging Technologies, and publish on DevPlan’s GCP portal, Medium, LinkedIn Articles, Reddit or Technology.org. Then reate a “project portfolio” on GitHub or LinkedIn that showcases your hands-on prowess. A high quality portfolio proves your expertise to future employers and gives you a competitive advantage over other job candidates. And third, Get Connected: Professional networking and referrals are the most effective method for landing your dream job - within your current employer or at a new company (rather than simply applying for jobs online and standing in the Human Resources queue with the masses). Join and participate in 2-3 high profile groups or professional associations such as Kaggle, the Data Science Central group on LinkedIn and Reddit’s Data Science sub-Reddit.

Get started today by enrolling in one or more training-certification programs:

• Applied Data Science Specialization 

• Data Analytics Professional Certificate 

• Data Science: Foundations using R Specialization

• Data Science Fundamentals with Python and SQL Specialization

• Data Science Specialization

• Data Visualization with Tableau Specialization

• Data Science with Python and R

• Google Data Analytics Professional Certificate 

• IBM Data Analyst Professional Certificate

• IBM Data Engineering Professional Certificate

• IBM Data Science Professional Certificate

• Microsoft Power BI Data Analyst Professional Certificate (Exam PL-300) 

• Understanding Data Science 

Enroll today (teams & execs are welcome).

Success awaits you! Lawrence E. Wilson - Data Science Academy (share & subscribe)

Data Structures (training)

Colleagues, in the “Data Structures” training program you will acquire high-demand skills in Java, Graph Theory, Data Structures, Algorithms, C++ (Programming Language), Theoretical Computer Science, Debugging, C (Programming Language), and Programming Principles. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. This program will address key technical questions involving: What is a good strategy of resizing a dynamic array? How priority queues are implemented in C++, Java, and Python? How to implement a hash table so that the amortized running time of all operations is O(1) on average? What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space. Skill-based training modules focus on: 1) Basic Data Structures, 2) Dynamic Arrays and Amortized Analysis, 3) Priority Queues and Disjoint Sets, 4) Hash Tables, and 5) Binary Search Trees, and 6) Binary Search Trees 2.

Enroll today (teams & execs welcome): https://imp.i384100.net/e1Pnk6 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Foundations: Data, Data, Everywhere

Colleagues, the “Foundations: Data, Data, Everywhere” course is part of Google Data Analytics Professional Certificate. Learn to: Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems, Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking, Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics, and Describe the role of a data analyst with specific reference to jobs. You will gain high-demand skills involving Data Literacy, Data Processing, Data-Driven Decision-Making, SQL, Data Visualization, Query Languages, Exploratory Data Analysis, Data Cleansing, Data Visualization Software, Data Collection, Spreadsheet Software, Data Ethics, Business Analytics, Data Analysis, and Analytics. Skill-based training lessons address: 1) Introducing Data Analytics and Analytical Thinking, 2) The Wonderful World of Data, 3) Set Up Your Data Analytics Toolbox, and 4) Become a Fair and Impactful Data Professional. By the end of this course you will: Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job, Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox, Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process, Evaluate the role of analytics in the data ecosystem, Conduct an analytical thinking self-assessment, and Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.

Enroll today (teams & execs welcome): imp.i384100.net/je6dXn 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Data Structures, Algorithms and Machine Learning Optimization (training)

Colleagues, the “Data Structures, Algorithms, and Machine Learning Optimization” program provides you with a functional, hands-on understanding of the essential computer science for machine learning applications. Learn "big O" notation to characterize the time efficiency and space efficiency of a given algorithm,  use Python data structures, including list-, dictionary-, tree-, and graph-based structures, understand the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing, implement statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving, grasp versatile (stochastic) gradient descent optimization algorithm works, and familiarize yourself with the "fancy" optimizers that are available for advanced machine learning approaches. Skill-based training modules cover: 1) Orientation to Data Structures and Algorithms - Machine Learning Foundations Series, A Brief History of Data and Algorithms, and their Applications to Machine Learning; 2) "Big O" Notation - Constant, Linear and Polynomial  Time, Common Runtimes, Best versus Worst Case scenarios; 3) List-Based Data Structures - Lists, Arrays, Linked Lists, Doubly-Linked Lists, Stacks, Queues, Deques; 4) Searching and Sorting - Binary Search, Bubble-Merge-Quick Sorts; 5) Sets and Hashing - Maps and Dictionaries, Sets, Hash Functions, Collisions, Load Factor, Hash Maps, String Keys, Hashing in ML; 6) Trees - Decision Trees, Random Forests, XGBoost: Gradient-Boosted Trees; 7) Graphs - Directed versus Undirected Graphs, DAGs: Directed Acyclic Graphs, Pandas DataFrames; 8) Machine Learning Optimization - Statistics versus Machine Learning - Objective Functions, Mean Absolute Error, Mean Squared Error, Minimizing Cost with Gradient Descent, Gradient Descent from Scratch with PyTorch, Critical Points, Stochastic Gradient Descent, Learning Rate Scheduling, Maximizing Reward with Gradient Ascent; and 9) Fancy Deep Learning Optimizers - Jacobian Matrices, Second-Order Optimization and Hessians, Momentum, and Adaptive Optimizers.

Enroll today (teams & execs welcome): https://tinyurl.com/yc2dfb8f 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Explore the “Data Driven Organizations” Amazon Audible & Kindle Book Series

Explore the “Data Driven Organizations” Amazon Audible & Kindle Book Series 

Data-Driven Organizations: 

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much success. Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

“The Promise of Data-Driven Decision Making - From Analytics to Visualization and Beyond” (audio & ebook)

Colleagues, “The Promise of Data-Driven Decision Making” (Audible) Kindle is a powerful resource that has the potential to transform the way we approach problem-solving in both our personal and professional lives. By collecting and analyzing data, we can gain valuable insights into the world around us, and use that knowledge to make more informed decisions. Throughout this book, we have explored the various aspects of data-based decision making, including the benefits, challenges, and best practices. We have also examined the different tools and techniques that can be used to collect and analyze data, as well as the ethical considerations that must be taken into account.

 Highly data-driven firms are three times more likely to report a major improvement in decision making, according to a large decision survey conducted by PWC. However, only one in three CEOs claim that their company is heavily data-driven. It comes up frequently in meetings with corporate leaders that executives have instant access to large volumes of data. We also learn that their personal intuition or gut feeling plays a significant role in their decision-making. How might the art and science of decision-making be combined better? A more efficient use of data and the capacity to draw insights are seen to present potential for enterprises to generate higher value. Analytics may support an organization's growth and innovation, increase productivity, and improve risk management when they are integrated into the culture of decision-making within the company. The use of facts, metrics, and data to inform strategic business decisions that are in line with a company's goals, objectives, and activities is known as data-driven decision-making. Interactive dashboards, work management platforms, and OKR tools are examples of modern analytics tools that assist individuals overcome prejudice and make the best management decisions that are in line with business strategies. Instead of making decisions based on intuition, opinion, or personal experience, it compiles historical data to examine trends and make better decisions for the future in relation to what has previously worked.

Listen today via Amazon Audible (https://tinyurl.com/ydbyh2t9) 

Or read now on Kindle (https://tinyurl.com/hptundzs) 

This book is part of the “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9