A data engineer designs, builds, and maintains data pipelines and infrastructure. They optimize data systems and ensure that data is readily available for analysis and decision-making.
Data engineers typically work full-time in office or hybrid setups, which often include flexible schedules. The field is often fast-paced, requiring data engineers to stay agile and responsive to meet project deadlines.
Exciting Parts of the Job
Enjoying the process of structuring raw data into meaningful formats
Doing a lot of organization
Finding satisfaction in seeing how your data work adds value to end-users or stakeholders
Having opportunities to learn new things as data and technology are always evolving
Challenging Parts of the Job
Dealing with the ambiguity of technical tasks
Solving complex issues such as data integrity, scalability, and performance optimization
Having meetings without clear agendas or outcomes
Education requirements:
A bachelor’s degree is typically required, with many positions also necessitating a master’s degree or relevant certifications.
Job outlook:
The future for data engineers is bright, with projected growth of 21% from 2018-2028, much faster than average for all occupations.
This is perfect for you if:
You enjoy problem-solving and finding solutions using data
You love technology and innovation
You are highly organized
You’re a passionate and continuous learner
If that sounds like you, definitely watch our interview with Rohit Jadhav to learn more about being a data engineer here!
Day in the Life
Schedule:
Data engineers often have some flexibility in their schedules. While they generally work full-time hours, they may have some freedom to manage their daily and weekly tasks within the framework of their team’s needs and project deadlines.
Setting:
Typically in offices or hybrid settings; however, fully remote work opportunities are becoming a trend.
Tasks:
Understanding project requirements, designing and implementing data pipelines and infrastructure, collaborating with stakeholders, and using agile methodologies to manage and iterate on projects.
Topics:
Data management, new database technologies, programming languages, machine learning algorithms, and AI models.
Skills:
Programming languages, data management, various data warehousing concepts, data visualization, and strong analytical skills to understand complex data problems.
Tools:
A laptop or computer equipped with tools like Amazon Web Services (AWS) and Redshift for storage and data warehousing, Apache Spark for data processing, and Airflow for orchestrating data workflows.
Common characteristics
Natural Talents (strengths):
Data engineers are naturally good at problem-solving and logical thinking. They pay close attention to detail, adapt easily to new data technologies, and are committed to continuous learning.
Interests:
Analyzing data, solving complex problems, and focusing on practical tasks. They thrive in structured environments, often following methodologies like Agile.
Personality:
Data engineers typically have investigative, realistic, and conventional personality types. They are logical thinkers, practical, and organized.
Values:
Integrity, reliability, teamwork, continuous improvement, and the ethical use of data.
*Terms used to describe interests and personality are based on the Holland Framework.
Pursuing majors in computer science, information technology, data science, mathematics, engineering, or statistics can be helpful.
Specialties and Similar Career Paths
Big Data Engineer
Business Intelligence (BI) Engineer
Cloud Data Engineer
Data Architect
Data Analyst
Data Integration Engineer
Data Science Engineer
Data Scientist
Data Warehouse Engineer
Machine Learning Engineer
Advice from Rohit
Job hunt tips to set yourself apart:
Focus on mastering essential skills, showcase your practical experience, and prepare thoroughly for interviews. Always continue to learn, as technology constantly changes, and maintain a proactive approach to networking.
Fastest way to get your foot in the door:
Learn essential skills like SQL and data warehousing, stay updated with industry trends through networking, and gain practical experience through projects and interviews. Continuously learn and apply for positions while refining skills to secure a data engineering role.
What Rohit would tell his younger self if he was just getting started:
“Meet people — LinkedIn is such a good platform, and I think you can extract a lot of value from it. Just explore something, understand what you want to do, meet people, and ask questions. There is a lot of information, and you have 80% noise out there—you’re looking for the 10%, and navigating that is very difficult. So get insight and experience from the person directly. It’s really helpful.”
