In this episode, I’m joined by Johannes Koch, Principal Engineer and AWS DevTools Hero, to talk about the real DevOps mindset, the evolution of developer experience, and how community work changed his career.

Johannes shares how starting in QA and support gave him a unique edge in understanding users, why building a proper CI/CD pipeline should come before writing code, and how the AWS Community Builders program helped him grow into his current role.

We also dive into:

• What DevOps culture actually means beyond automation
• His take on GitOps and why CI/CD is still underrated
• Behind the scenes of the AWS Heroes Program
• Creating content, mentoring others, and avoiding the trap of tech hype

Whether you're starting out in DevOps or deep into cloud architecture, Johannes' insights are packed with value for data and AI professionals.

Links:

• Johannes on LinkedIn
• YouTube: @cicdonaws
• AWS Community Day DACH

In this episode, I sit down with Deepak Goyal, the founder of AzureLib, to talk all things data engineering, cloud platforms, and how to teach the next generation of engineers.

We explore why Azure became his go-to, how real-world projects beat theory every time, and why tools like ChatGPT are great assistants, but no substitute for structured learning and solid fundamentals.

Follow Deepak on LinkedIn: https://www.linkedin.com/in/deepak-goyal-93805a17/

Check out AzureLib: azurelib.com

Learn Data Engineering with me: learndataengineering.com

In this episode of the Plumbers of Data Science podcast, I dive into the challenges of recruiting today, from overwhelming job application volumes to reaching out directly to recruiters.

I’m testing new strategies to make the process smoother for everyone involved, focusing on fresh job listings and fostering connections with hiring managers who need skilled engineers. My goal? To secure five job placements in Germany by year’s end!

Have thoughts on today’s job market, or tried the Easy Apply feature yourself? Drop a comment below—I’d love to hear your experience!

In this Hero Talk episode, I had the pleasure of chatting with Ben Rogojan, better known as the "Seattle Data Guy." Ben is a data engineer, YouTuber, and freelancer with a background at Facebook. He's become a go-to expert on freelancing for engineers, particularly in the data space.

We dive into Ben's journey from being a full-time engineer to making the switch to freelancing, how he built his own business, and the unique challenges freelancers face in this space.

We also explore how to break into freelancing, the value of specializing in a specific skill, and practical tips on landing your first freelance clients.

In this episode of the Plumbers of Data Science podcast, I'm sharing some exciting updates about the future of Learn Data Engineering and a big new service we’re launching—recruiting!

I explain how this new offering will help engineers find their next career move while connecting companies with top talent. Tune in to hear more about how the Academy, Coaching, and now recruiting fit together into one ecosystem designed to support your career growth.

Let me know your thoughts in the comments—are you excited about this new direction?

In this episode of the Plumbers of Data Science podcast, I’m sharing my thoughts on why data modeling isn’t as complicated as people make it out to be. You hear about courses and tutorials that stretch for hours—but is it really that hard?

I’ll break down the two main things you need to focus on when modeling data and explain why, once you’ve got those down, the rest falls into place.

In this Hero Talk episode, I talk with Mezue, a seasoned Data Engineer with expertise in Azure Databricks Data Engineering. We cover his journey from Electrical Engineering to Data Engineering and discuss the key skills, like Python, SQL, and Spark, that are essential in the field.Mezue also shares his experience running an Azure Databricks bootcamp and offers advice on how to break into Data Engineering, especially in Cloud environments. We also touch on the challenges of finding junior roles and how to stand out by working on practical projects.

In this Hero Talk episode, I chat with Susan Walsh, the “Classification Guru,” known for her expertise in cleaning and classifying messy data.

We dive into her unexpected journey into the data world, starting with a spend analytics job, and how that led to her founding her own business focused on dirty data. Susan shares the unique challenges businesses face with poor data quality, explaining why 99.9% of data problems are actually people problems.

We also explore practical ways to deal with these issues, such as finding those "crappy" data cleaning jobs to gain experience, and the importance of consistent data maintenance to prevent future headaches. From addressing dirty CRM systems to battling fraud, Susan’s stories highlight how critical clean data is for business success.

In this Hero Talk episode, I sit down with Paolo Lulli, an experienced Data Engineer, to explore some of the core challenges and decisions in API development and data management. We dive deep into the debate between serverless infrastructure versus traditional servers, discussing the pros and cons of both approaches, particularly in the context of scalability, cost, and maintenance.

Paolo also shares his hands-on experience with time series databases, explaining their advantages in handling massive amounts of data from IoT devices. We delve into vendor lock-in issues, highlighting how relying too heavily on cloud providers like AWS or Azure can impact long-term flexibility.

In this episode of the Plumbers of Data Science podcast, I’m diving into why testing can be so challenging for data engineers. The inspiration for this topic actually came from one of my recent Coaching sessions, where the question of test-driven development (TDD) came up during a Q&A. It stuck with me, so I thought it would be a great topic to dive deeper into.

I’ll explain the key benefits of TDD, like improved code quality and easier refactoring, and why, despite its advantages, it’s not always widely adopted—especially in fast-paced environments where time constraints dominate. We’ll also talk about the specific challenges data engineers face with TDD, such as handling large, unpredictable data, integrating with external systems, and adapting to ever-changing data.