Production data engineering looks nothing like the tutorials. Datasets are messier, pipelines break in ways no course prepares you for, and if a dashboard is empty at 8:00 AM, people start asking questions immediately.

Kavya Kumari, Senior Data Engineer at Tenjumps, made the transition from learning environments to managing live, high-pressure pipelines firsthand. In this conversation, she shares what that shift actually feels like, the lessons that stuck, the mistakes that taught her the most, and practical advice for anyone preparing to make the same jump.

If you are a bootcamp graduate, career changer, or junior data engineer about to touch your first production system, this will feel very relatable.

What is the biggest difference between learning environments and production systems?

In learning environments, datasets are small, clean, and easy to work with. In production systems, you deal with millions of records, messy schemas, and data sources that change without warning. Pipelines that worked perfectly in development can behave very differently at scale.

Beyond the technical challenges, there is stakeholder pressure. If a dashboard is empty at 8:00 AM, people immediately start asking questions. In those moments, the data engineer is responsible for making sure the data is available and reliable.

What surprised me the most was realizing that the real job is not just building pipelines. It is making sure they run reliably every single day.

Which skills from your learning phase actually mattered in production?

Understanding SQL well, especially joins and aggregations, turned out to be extremely important. The practice projects where I built small ETL pipelines using Python and Spark also helped because they gave me confidence working with data transformations. Through those projects, I learned core pipeline concepts: ingestion, transformation, and storage layers.

At the same time, I realized that understanding how data flows through a system matters even more than knowing syntax. My personal projects gave me clarity on how pipelines work end to end. That experience made me much more comfortable when I started my first data engineering job.

What do tutorials and courses not prepare you for?

In courses, everything works smoothly. APIs respond perfectly, schemas stay consistent, and jobs run without issues. In production, you deal with broken APIs, missing data, unexpected schema changes, and legacy pipelines written years ago by someone who is no longer around to explain them.

Another thing that caught me off guard was the sheer amount of debugging and investigation work involved. Sometimes you spend hours just trying to figure out why a pipeline failed. Courses teach tools, but they do not fully prepare you for operating and maintaining systems over time.

"Failures are normal in production. Good logging and monitoring make it much easier to find and fix problems."

How did you handle your first production pipeline failure?

It was a scheduled job that loaded data into a table used for morning dashboards. One day the pipeline failed because a third-party API returned incomplete data, which caused a transformation step to break.

I knew dashboards depended on that data and people would notice if the numbers were missing. I started checking the logs and followed the pipeline steps to find where the problem happened. After identifying the issue, I added simple validation checks to handle missing fields and reran the pipeline successfully.

That experience taught me that failures are normal in production data engineering. The difference between a stressful failure and a manageable one comes down to good logging and monitoring.

What would you tell your past self when you were just starting?

Focus on understanding systems rather than just tools.

When I started learning data engineering, I spent a lot of time worrying about learning every new framework or technology. Over time I realized that chasing every new tool is not necessary. What really matters is understanding how data pipelines work end to end, from ingestion to transformation, storage, and monitoring.

Strong fundamentals like SQL, Python, and basic systems thinking are much more valuable than knowing the latest framework. Building small projects and practicing real scenarios helped me understand things far better than reading documentation alone.

How do you continue learning while working in production?

Balancing work and learning is challenging. What helped me was setting aside small but consistent learning time, even if it is just an hour a few times a week. I also try to connect what I am learning with real problems from work, which makes the learning more practical.

For example, while working with data pipelines, I started exploring Databricks more deeply to better understand performance optimization. Having a specific goal, like preparing for a certification, keeps learning structured. Otherwise, it is easy to end up scrolling through tech blogs without actually building or practicing anything.

What are you working on right now to level up?

Right now, I am focused on distributed data processing and pipeline performance optimization. Since I work with Databricks, I am exploring its newer features and spending time experimenting with query optimization, better data modeling, and improving pipeline efficiency.

My goal is to build pipelines that are not just working, but efficient and scalable as data grows.

Your turn

What is one lesson you have learned in your own data engineering journey that tutorials never covered? And what question would you add to this list? We would love to hear from you.