Fusemachines recently conducted a webinar on pursuing data engineering as a career. Facilitated by our VP of Data Engineering Vicente Manuel Rustarazo Hervás, the event had more than 200 participants. The interactive session empowered aspiring data professionals, students, and enthusiasts alike to envision their own paths in this dynamic field.
Here are our takeaways from the session.
Why is now the time to pursue a career in data engineering?
- High demand: Data engineering is in high demand as organizations increasingly rely on data-driven insights for decision-making.
- Growing data complexity: The exponential growth of data and its complexity requires skilled data engineers to design and maintain robust data infrastructure.
- Career versatility: Data engineering skills are transferable across industries, offering diverse career opportunities and growth potential.
- Competitive salaries: The scarcity of skilled data engineers results in attractive compensation packages and benefits.
Where to start?
To start a career in data engineering, focus on building a strong foundation in computer science, including programming, algorithms, databases, and data structures. Gain proficiency in languages like Python, SQL, and Java.
Next, familiarize yourself with essential data engineering tools and technologies such as Apache, Hadoop, Spark, Kafka, SQL databases, and cloud platforms like AWS, GCP, or Azure. Practical experience with these tools will help you handle large-scale data processing, storage, and analysis.
Acquire database and data modeling skills to design efficient and scalable data schemas. Gain hands-on experiences through projects, internships, or real-world data challenges. Continuously learn and stay updated with the latest trends and advancements in data engineering by exploring online resources and taking relevant courses.
Lastly, networking is key. Connect with other professionals in the field, attend industry events, and utilize platforms like LinkedIn to build relationships and seek mentorship. Pursuing certifications such as AWS Certified Big Data – Specialty or Google Cloud Certified – Professional Data Engineer can enhance your credibility.
Career path of a data engineer
Essential Q&A from the webinar:
How does a data engineer work with other team members like ML engineers and software engineers?
Data engineers collaborate closely with other team members, including ML engineers and software engineers, to ensure the successful development and deployment of data-driven applications and machine learning models. They work together to ensure data is efficiently extracted, transformed, and loaded (ETL) into the required formats for further processing and analysis. Similarly, They collaborate to establish the necessary data pipelines, ensuring that the input data required by the models is readily available.
By leveraging their respective skills and expertise, they can create robust, scalable, and high-performing data systems and applications.
How can a person coming from a non-technical background pursue a career in data engineering?
The steps to pursuing a data career from a non-technical background are similar to the steps mapped out above. It’s important to build a solid foundation of basic skills and knowledge, no matter your position or responsibilities. You can then choose roles that require less technical expertise, such as data governance analyst, data compliance officer, data journalist, or data insights manager.
Bottom line
Our webinar on data engineering as a career offered valuable benefits to all attendees. It created awareness, provided guidance on career paths, explored industry expertise, and enabled interactive sessions for clarifying doubts. Follow our social media for more events like this in the near future.
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