What is a Data Engineer?
Page Last Updated: June 2025
“Data engineering is much more than loading data into a database” – Frank Kane (Ex-Amazon Sr. Engineer, CEO Sundog Education)
Data drives the modern world. From powering personalized Netflix recommendations to enabling faster fraud detection in banking, data is at the heart of nearly every business decision. But how does raw data turn into actionable insights? This is where data engineers step in.
A data engineer is responsible for designing and maintaining systems that collect, store, and transform vast amounts of data into a usable format. Their work is the backbone of data-driven decision-making, enabling businesses to derive insights efficiently and stay ahead in competitive markets.
If you’re considering a career in this field or curious about how it shapes the tech world, keep reading. We’ll explore what a data engineer does, the tools they use, key career opportunities, and how you can become one.
What Does a Data Engineer Do?
Data engineers specialize in building and optimizing the systems that make data accessible for analytics and operational workflows. Their core responsibilities include the following tasks:
Building and Maintaining Data Pipelines
A primary duty of data engineers is to design and implement pipelines that move data from various sources (e.g., applications, devices, databases) into data warehouses or lakes (more structured, cleaner data sources). These pipelines handle everything from data ingestion to processing and transformation, ensuring data is clean, structured, and ready for use.
Collaboration Across Teams
Data engineers work closely with data scientists, analysts, and software engineers. They ensure that the infrastructure and data pipelines align with the analytical and operational needs of businesses. For instance, data scientists rely on engineers to supply well-processed data for advanced modeling, while analysts use their pipelines for reporting.
Ensuring Data Quality and Availability
High-quality, reliable data is critical for decision-making. Data engineers are responsible for creating systems that monitor and enforce data quality, ensuring accuracy and accessibility at all times.
Real-World Example
Imagine an e-commerce platform experiencing spikes in traffic during Black Friday. A data engineer ensures that real-time data about product availability, sales trends, and customer behavior is captured, processed, and immediately accessible to marketing teams for decision-making.
A Day In The Life of a Data Engineer With Frank Kane
In the real world, there’s a lot more than just designing big new systems, although building new data pipelines is certainly part of the job! As new data becomes available, you’ll need to ingest the data somehow, store it in some sort of distributed data store, and impose some sort of schema on it so it can be queried. That’s the fun part.
Troubleshooting existing systems is a big part of the job too. Whenever you are dealing with large, distributed systems and messy data, there’s a lot of potential for things to go wrong. Perhaps a data feed has gone down or become corrupt for some reason. Perhaps you’ve hit resource limits somewhere, or you’ve hit some sort of processing bottleneck you hadn’t encountered in the past. Perhaps there is a huge surge of data that you don’t have capacity for, or perhaps something happened that drove up your cloud computing costs that requires rapid optimization of the systems you have. Having deep experience and education equips you with the knowledge to resolve such problems quickly, and the certifications for data engineering often focus on your ability to diagnose these sorts of issues.
And as with any tech job, a fair amount of the day will involve collaborating with your peers, checking their work, updating documentation or project management tools, and the usual meetings you see in any business.
Courses by Frank Kane
Key Skills and Tools for Data Engineering
To succeed as a data engineer, you’ll need both technical proficiency and a deep understanding of the tools shaping the data engineering landscape.
Programming Languages
- Python: A programming language for data manipulation and pipeline development.
- SQL: A query language essential for working with relational databases to extract, transform, and load (ETL) data.
- Java: A programming language often used alongside big data tools for building distributed systems.
Data Pipeline Tools
Modern data engineers rely on tools like Apache Airflow, Kafka, and Spark to build scalable, robust pipelines. These tools enable automation, real-time processing, and efficient handling of massive datasets.
Cloud Platforms
With businesses increasingly adopting cloud-native systems, cloud platforms such as AWS, Google Cloud (GCP), and Microsoft Azure are invaluable. They provide scalable infrastructure for building and managing data pipelines and processing workflows.
Data Warehousing and Architecture
A solid understanding of data architecture and warehousing tools (e.g., BigQuery, Snowflake, Amazon Redshift) is also vital for storing and organizing data effectively.
Learn more about essential tools and their applications here: Data Engineering Courses on Udemy.
What is Data Engineering in the Modern Tech Stack
Data engineering has evolved alongside the big data revolution. Here’s a look at its role in the modern tech stack.
Data Engineering vs. Data Science
While data scientists analyze data for actionable insights, engineers focus on creating and managing the infrastructure that allows data scientists to do their work efficiently. Think of it as building the roads versus driving the cars.
Role in Big Data and Cloud Systems
Data engineering supports big data workflows by designing scalable systems that process billions of data points. With the rise of cloud-native platforms, engineers now build systems optimized for distributed computing, ensuring faster processing and lower costs.
AI and Data Engineering
AI is reshaping data engineering by automating certain routine tasks and enabling smarter data pipelines. For example, AI tools can enhance data quality and governance by detecting and correcting anomalies or inconsistencies in data.
Career Paths and Opportunities in Data Engineering
The field of data engineering offers diverse roles and competitive salaries.
Common Career Pathways
- Entry-Level: Junior Data Engineer or ETL Developer
- Mid-Level: Analytics Engineer or Data Platform Specialist
- Senior-Level: Data Architect or Infrastructure Lead
Industries Hiring Data Engineers
Data engineers are in demand across all sectors—including fintech, healthcare, eCommerce, and entertainment. The demand for their expertise peaks in areas reliant on big data analytics and personalization.
Salary Insights
Starting salaries for data engineers average around $90K per year and can exceed $150K for seasoned professionals. The job outlook remains strong as companies increasingly adopt data-driven strategies.
Check out the Data Engineering For Beginners course to kickstart your career.
How to Become a Data Engineer Learning Pathways and Courses
Breaking into data engineering can seem daunting, but a clear roadmap and the right resources make the transition manageable.
Suggested Learning Roadmap
- Start with foundational languages like Python and SQL. I recommend learning both of these to be competitive.
- Master data modeling and warehousing concepts.
- Learn tools like Apache Spark and Airflow for data pipelines.
- Gain expertise in cloud platforms and streaming tools.
- Build projects to showcase your skills, such as creating a data warehouse for analytics or implementing a data streaming solution. There’s a lot of free-to-use data available on the internet to make an interesting project with. Even if the dataset isn’t particularly large, these projects will help you understand how the pieces fit together, and demonstrate to employers that you know how to build pipelines.
Certifications to Consider
Certifications demonstrate expertise and enhance employability. Consider these popular credentials:
- AWS Certified Data Engineer – Associate: Gain proficiency in AWS data tools. Browse AWS data engineer courses.
- Google Cloud Professional Data Engineer: Focuses on scalable and reliable data solutions. Learn more.
- Microsoft Azure Data Engineer Associate (DP-203): Specializes in data transformation and analytics in Azure. Explore courses.
- Databricks Certified Data Engineer Associate: Aimed at those working with big data systems in Databricks. Get certified.
The Value of Online Learning
Don’t have a computer science degree? No worries. Certifications, online courses, and real-world projects can help you build the skills employers seek. Past career experience and self-taught expertise also stand out to hiring managers. Sites like Udemy offer flexible learning options tailored to your pace and schedule.
Frequently Asked Questions
Do Data Engineers Code?
Yes! Data engineers write code for building pipelines, automating workflows, and transforming data. Python and SQL are particularly vital for their tasks.
How Long Does It Take to Become a Data Engineer?
It varies depending on prior experience. For beginners, mastering key skills and tools can take 6–12 months with consistent effort.
Is Data Engineering in Demand?
Absolutely! With businesses prioritizing data-driven decision-making, data engineering has become one of the most sought-after tech careers.
What Degree Do You Need to Be a Data Engineer?
While a degree in computer science or related fields can help, many succeed through certifications, hands-on projects, and professional experience. You don’t need to go back to school if you already have a degree in another field. Online platforms like Udemy provide everything you need to get started.
Data Engineering Unlocks a World of Possibilities
Data engineering is a dynamic, challenging, and rewarding career path. By building systems to handle complex data flows, data engineers power decisions that shape industries.
Want to master data engineering? Start your learning journey today with Udemy’s Data Engineering Courses. Prepare for certifications, build real-world projects, and position yourself among the most in-demand professionals in tech.
