In today’s data-driven world, the demand for skilled Python developers who can work across backend systems, pipelines, and data platforms is growing fast. But what does it take to succeed in this space—especially as a consultant in the U.S.?
In this blog, we meet one such professional who began as a backend Java developer and gradually transitioned into a data engineering role, focusing strongly on Python, Airflow, and validation frameworks. Their journey is a testament to how curiosity, continuous learning, and wise tooling choices can shape a rewarding career in tech consulting.
Whether you’re early in your career or looking to shift from development to data, the insights shared here will help you chart your path forward.
(Note: For confidentiality, we’ve kept the consultant’s identity and client details anonymous.)
Q: Let’s start with your current role. What kind of work are you doing as a Python developer?
I’m currently working in a data engineering role focused on backend development using Python. My responsibilities include maintaining and optimizing ETL pipelines, utilizing tools such as Airflow and Marshmallow for scheduling and validation, and refactoring legacy code to ensure improved performance and scalability. My role involves ensuring the data infrastructure runs smoothly and efficiently.
Q: Were you always in backend and data-focused roles, or did you transition over time?
I started as a Java developer. Over time, I became more involved in backend API development and gradually transitioned into working with Python. The more I explored Python, the more I realized how effective it was for data engineering tasks—especially with frameworks like Airflow, which is now central to my work.
Q: What triggered your interest in learning Python and working in data engineering?
When I first encountered Python, I was impressed with its simplicity and power. It had a wide range of libraries for data processing, which made it ideal for backend work. I had the opportunity to work on several data-intensive projects, which is when I truly became familiar with Python for data pipelines and transformations. It felt like a natural evolution from my Java experience.
Q: What tools and technologies are part of your current tech stack?
My core stack includes Python for scripting, Airflow for pipeline orchestration, and Marshmallow for schema validation. We also utilize Docker for containerization and GitHub Actions for continuous integration and continuous deployment (CI/CD). Everything is built to run efficiently in the cloud, and we continually optimize for faster deployments and smoother data handling.
Q: Can you tell us more about the ETL pipelines you work on? What do they involve?
The pipelines are built to pull data from multiple sources, process it through various stages, and load it into a target system. I work on writing the logic that handles these transformations, ensures data integrity, and triggers the next steps in the pipeline. We’ve also built validation layers using Marshmallow to catch errors early and improve reliability.
Q: Any recent project that you’re particularly proud of?
Yes, we recently overhauled an entire pipeline system to reduce processing time. Previously, it would take hours to validate and process specific datasets. By introducing caching layers and optimizing our schema checks, we were able to cut that time in half. It was rewarding to see the business benefit directly through faster reports and better data accuracy.
Q: How do you approach debugging or solving technical roadblocks?
First, I reproduce the issue locally. I’ll use logging and step-through debugging to isolate where the issues are occurring; tools like Postman are helpful when dealing with APIs. Once I narrow it down, I review the documentation and search for similar issues in community forums. If I still can’t solve it, I reach out to my peers—we have a very collaborative culture.
Q: What would you say are your top learning resources?
Hands-on experience has taught me the most. Aside from that, I use the official Python documentation, Stack Overflow, and Medium blogs by other developers. I also follow GitHub repositories for tools I use, so I stay updated on the latest releases and best practices.
Q: Any certifications or structured learning that helped you grow?
Yes, I completed a few online courses on Udemy focused on Python for Data Engineering. They helped me understand the end-to-end lifecycle of data projects. I’m also exploring certifications related to cloud services, as they are becoming increasingly relevant in our field.
Q: What advice would you give to someone trying to break into backend development or data engineering?
Start small. Build simple projects—maybe a basic ETL pipeline using open-source data. Get comfortable with one programming language, such as Python. Then, explore tools such as Airflow, Docker, and validation libraries. And always write clean, testable code. Version control and documentation matter just as much as logic.
Q: Do you think being a consultant changes how you approach your work?
Definitely, as a consultant, there’s always a focus on outcomes and value. You’re not just building for the sake of it—you’re solving real business problems. That mindset has enabled me to grow more rapidly, adapt quickly, and learn how to effectively communicate technical concepts to non-technical stakeholders.
Q: What’s next for you? Are there any areas you’re excited to explore?
I want to go deeper into cloud-native data engineering. Tools like AWS Glue, Redshift, and EMR are on my radar. I’m also curious about how machine learning (ML) pipelines integrate with data engineering, so I plan to learn more about MLOps soon.
Conclusion:
From writing Java code to building robust ETL pipelines in Python, this consultant’s journey is an excellent example of how career paths in tech are rarely linear. With the right mindset, continuous upskilling, and a focus on solving problems that matter, it’s possible to grow into high-impact roles across industries. If you’re thinking about transitioning into data engineering or want to sharpen your Python skills, take this as your sign to start.
Are you also looking to land your next consulting opportunity in data engineering? Start by exploring open roles today.
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