Data engineering is one of the most promising careers in the tech domain today. If you want to pursue a career in this field, then you need to understand the data engineer career path to understand how you can progress.
Understanding this career path will help you understand the roles and responsibilities you might have to fulfill in the future. This way you can upskill yourself and learn new things that could help you improve your performance in future roles.
In this article, we present the data engineer career path. We discuss the junior, mid, and senior-level positions.
We will also throw light on their roles and responsibilities and why should you become a data engineer, along with the average salary of a data engineer. We also briefly highlight the skills you need to land such a role.
Also read:Â What Is Data Engineering: A Complete Guide in 2024
What Does a Data Engineer Do?
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With technological advancements and the increasing role of data in business decision-making, the demand for data engineers is rapidly growing. They work in different settings to build systems that help collect, manage, and convert the raw data and provide meaningful insights.
Data engineers analyze the data and provide their insights to help business leaders make effective decisions to optimize the company’s performance.
The following are some of the key responsibilities and tasks of data engineers:
- Collect and analyze data to extract actionable insights
- Develop algorithms that convert the raw data into meaningful and actionable information
- Build, test, deploy, and maintain database pipeline architectures
- Collaborate with business stakeholders and leaders to understand company objectives
- Ensure processes comply with data governance and security policies
Why Pursue a Career in Data Engineering? (will be brief)
Today, a career in the data domain can be both challenging and rewarding. As a data engineer, you will be at the center of data collection and analysis that drives business decisions.
You will simplify data scientist’s access to the data so that it can be analyzed effectively and help decision-makers to make the right decisions. For this role, skills such as problem-solving and coding will be most helpful.
According to Dice, data engineering is one of the fastest-growing tech professions in the world. In addition, a significant rise in the number of interviews for data engineers has been observed, further signifying that it is a fast-growing profession.
Also read:Â The Ultimate Data Engineer Interview Guide
Data Engineer Salary
According to BuiltIn, the average salary of a data engineer in the US is $125,040. While Salary.com states the minimum salary is $116,881 and the maximum is $143,567.
The following table shows the average salary of a data engineer in different regions of the US.
Source: BuiltIns
Data Engineer Career Path: The Roadmap
As a data engineer, you will have a chance to design and build data applications. As the importance of data has grown, this field has become highly competitive as well. Companies now seek individuals with more than tech skills, they prefer a candidate with an understanding of data pipelines and how they deliver value.
Typically, new graduates or freshers start in an entry-level or internship position. They tend to be given ad-hoc and smaller projects that are of less importance for the company, so they can learn.
However, as their career progresses, they move ahead and start taking more hands-on roles in the company. They start to take greater responsibilities and manage the overall architecture of the data pipelines.
Let’s look at the data engineer career path.
Junior Data Engineer
This is typically where a data engineer starts from. This job generally involves fixing bugs and carrying out small task-oriented projects with minimum impact on the company and its performance. They are mainly responsible for maintaining the data infrastructure.
A candidate with a bachelor’s degree in computer science or related fields can apply for this position. Skills such as database management, data warehousing, software development, etc. can be helpful for this position.
Their job description includes:
- Assist in integrating data from different sources
- Perform data quality checks
- Collaborate with senior and experienced engineers and other stakeholders
- Design and develop data pipelines and databases
Mid-Level Data Engineer
Once you have spent a certain time (years) as a junior data end engineer, then you can move to the mid-level role. Depending on the company and the engineer, you can land titles like data warehouse engineer, senior data engineer, etc.
In such positions, you will play a more task-oriented role and work on ad-hoc projects. You will have to be more proactive, take on more project management roles, and collaborate with different departments.
As a mid-level data engineer, you will also work closely with product managers and data scientists to design and build business and product solutions.
Typically, you will work for 3-5 years in this role.
The following are some of the key responsibilities of a mid-level data engineer:
- Design and manage data infrastructure
- Oversee ETL operations
- Improve data models and architectures
- Ensure data security and quality
- Collaborate with experts like data scientists, analysts, etc.
Senior Data Engineer
The senior data engineers build and maintain systems for collecting data and the pipelines. It is more of a cross-functional role that requires collaboration with several stakeholders like data science and analytics teams.
They have to develop data pipelines for deeper analysis and testing.
The following are some key responsibilities of senior data engineers:
- Provide mentoring to teams of data engineers
- Build and implement complex data architectures
- Manage different big data platforms
- Collaborate with business stakeholders and different departments to develop & deploy the company’s objectives
- Ensure the scalability, performance, and security of data systems
What’s Next?
You can work in different roles in the domain after becoming a data engineer. Let’s look at some of the positions that you can hold as a data engineer.
- Chief Data Officer:Â This is an executive-level role where you will be responsible for every data-related activity across the company. You will create strategies, and oversee data governance. In addition, you will also take on a more proactive approach to data analysis and business intelligence. The goal of this position is to align data with business strategies.
- Data Engineering Manager: Many senior data engineers move to more managerial roles and oversee the company’s data engineering department. As a data engineering manager, you will provide leadership to a team of data engineers, coach them, and drive their vision to align with the company’s goals.
- Data Architect:Â They work closely with data engineers. As a data architect, you will provide a blueprint to the engineering team to build advanced data models and pipelines.
Also read: “This is how I landed a big data engineer job at Facebook amid layoffs and hiring freezes” — Derek Chung
Skills to Become a Data Engineer
As a data engineer, you should have a wide array of skills and capabilities to perform better and contribute to the company’s goals. The data engineer career path goes through learning different sets of skills so you can adjust to different situations and still give your best.
Even though the skills required by a data engineer change with time and technological developments, the following are some of the most important ones:
- Designing and building large-scale application
- Data modeling and mining
- Statistical modeling and regression analysis
- Excellent coding skills
- Proficiency in R, Python, C, C++, Java, and other programming languages
- Data cleaning and analysis
- Machine learning
How Interview Kickstart can Help you Become a Data Engineer?
As newer technologies emerge, the role of the data domain will continue to rise and the competition for fields such as data engineer will also increase. Interview Kickstart’s Data Engineering Interview Masterclass will help you land your dream role.
Enroll in this course to crack even the toughest technical interviews and give a boost to your career. Our instructors are experienced data engineers who will help you learn the key concepts, create ATS-clearing resume, build an online personal brand, and optimize your LinkedIn profile.
Interview Kickstart Reviews is a testament to how our data engineering interview masterclass and other courses have helped them land their dream jobs.
FAQs: Data Engineer Career Path
What Educational Background is Required to Become a Data Engineer?
A bachelor’s degree in computer science, information technology, software engineering, or a related field is typically required. Some roles may also require a master’s degree or relevant certifications.
Are there Specific Certifications that can Help Advance a Career in Data Engineering?
Yes, certifications such as Google Cloud Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate, AWS Certified Big Data – Specialty, and Databricks Certified Data Engineer Associate can be valuable.
What are Some Common Tools and Technologies used by Data Engineers?
Data engineers often use tools and technologies like Apache Hadoop, Apache Spark, SQL, NoSQL databases (e.g., MongoDB, Cassandra), ETL tools (e.g., Talend, Informatica), cloud platforms (e.g., AWS, Azure, Google Cloud), and programming languages like Python, Java, and Scala.
How Important is Knowledge of Big Data Technologies for a Data Engineer?
Knowledge of big data technologies is crucial as data engineers often work with large datasets that require efficient processing and analysis. Familiarity with tools like Hadoop, Spark, and Kafka is often expected.
What is the Difference Between a Data Engineer and a Data Scientist?
Data engineers focus on building and maintaining the infrastructure and architecture for data generation, whereas data scientists analyze and interpret complex data to provide insights and solutions. Data engineers ensure data is accessible and clean for data scientists to analyze.
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