One Week to Rebuild My Python Foundations as a Data Engineer

Relearning Python, one day at a time.

After working as a Data Engineer for a few years, I realized I hadn’t been using Python as much lately.
When I returned to hands-on coding, I noticed that some of the fluency I once had with the language had faded. My syntax memory slowed down, and even simple pandas transformations took me longer to recall.

So I decided to dedicate one focused week to refresh my Python foundations.
Not to start from scratch, but to reconnect with the essential skills that make everyday data engineering smooth and productive.

Why I Did This

As data engineers, we often focus on infrastructure, orchestration, and tooling. But at the core of all that is Python, the language that connects APIs, databases, and data pipelines.

Taking a step back to rebuild that foundation felt necessary. I wanted to:

• Relearn best practices and clean coding habits

• Regain confidence in manipulating data with pandas

• Reconnect with the flow of building scripts from scratch

To make it structured and achievable, I created a one-week Python refresher, focusing on the skills data engineers use daily, from file handling and data cleaning to working with databases and building ETL pipelines.

My 7-Day Plan

Each day had a clear goal with short, hands-on exercises that built on the previous day’s work.

By the end of the week, I had a simple but functional ETL pipeline that:

• Fetched JSON data from an API

• Cleaned and normalized it with pandas

• Logged every step of the process automatically

• Loaded it into PostgreSQL

Tools & Libraries I Used

• Python 3.11

• pandas for data cleaning and transformations

• requests for API calls

• SQLAlchemy and PostgreSQL for database integration

• logging for tracking ETL execution

You can find the full project and daily breakdown here:
👉 github.com/nenalukic/python-foundations-refresher

What I Learned (Again)

1. Clean Python code never goes out of style.
   Good habits like clear naming, modular functions, and structured error handling make everything easier to maintain.

2. Readable pandas code is more valuable than clever tricks.
   Simplicity often wins when you return to your code weeks later.

3. Small, complete projects build momentum.
   A single working ETL pipeline teaches more than a dozen disconnected tutorials.

4. Confidence comes from repetition.
   Writing, testing, and refining code daily helped bring back fluency and flow.

Who This Is For

This type of focused refresher is great for:

• Data engineers who haven’t coded in Python for a while

• Analysts transitioning into engineering roles

• Developers looking to strengthen data handling and SQL integration skills

• Anyone who wants to rebuild technical confidence through practice

It’s not about learning new tools; it’s about reconnecting with what you already know, and mastering it again.

Final Thoughts

Sometimes, the best way to move forward is to slow down and rebuild your foundations.
Spending one week revisiting Python didn’t just refresh my skills, it restored my confidence and curiosity.

If you’ve stepped away from coding or feel rusty, give yourself one structured week.
You’ll be surprised how quickly it all comes back once you start building again.

Project Repository: github.com/nenalukic/python-foundations-refresher

💬 I’d love to hear how you approach skill refreshers. What do you do to keep your technical edge sharp?

If you need to practice Python more, I highly recommend this book:

“Python Data Engineering Resources”