DEV Community: Juliana Albertyn

Job Hunting in Tech: Small Things That Make a Big Difference

Juliana Albertyn — Fri, 10 Apr 2026 10:46:12 +0000

Over the past few months, I’ve emailed my CV to a lot of agencies. Only one sent an automated “we received your CV” reply - and that tiny gesture already stood out. Or when you submit your CV on a website, some sites only say "Submitted", while others say "Thank you". It’s amazing how far basic acknowledgement goes when you’re job-seeking.

I’ve also found that a quick phone call with an agency can change the whole experience. Even a brief, honest response - “We don’t handle remote roles” or “This one won’t be a good fit” - shows respect. It makes me feel that my time matters too.

Then there’s the salary question. We’re often told not to bring it up first, but that can leave both sides guessing. It’s like listing a PlayStation on Facebook Marketplace with photos, specs, accessories - but no price. A buyer messages you, you chat, you arrange to meet… and only then do you reveal the cost.

He can’t afford it.
You both wasted your time.

None of this is complicated.
A quick acknowledgement.
A short human conversation.
Transparent expectations.
Small things — but they make the whole process feel more balanced, and a lot more humane.

My first Python project: Excel to SQL pipeline (feedback welcome)

Juliana Albertyn — Mon, 30 Mar 2026 06:20:44 +0000

I’ve just made my first substantial Python project public. It’s an Excel-to-SQL pipeline focused on cleaning messy spreadsheet data and preparing it for database ingestion.

This is still a work in progress, and I’m actively improving it.

Problem

Working with Excel data often means dealing with:

Inconsistent date and time formats
Mixed data types in a single column
Missing or malformed values
Subtle issues that only surface during database insertion

I wanted a reusable way to clean and standardise this kind of data before loading it into SQL.

Approach

The project focuses on:

Column-wise cleaning functions (dates, times, text, etc.)
Configurable parsing with strict vs permissive modes
Clear error reporting with row-level context
Separation between cleaning logic and pipeline orchestration

The goal is to make the pipeline predictable and easier to debug when something goes wrong.

Example

Input (Excel):

Mixed date formats
Numbers stored as text
Invalid values scattered through columns

Output:

Cleaned, typed data ready for SQL insertion
Invalid values either coerced or flagged, depending on mode

What I’m unsure about

I’d really value feedback on:

Code structure and modularity
Naming conventions and readability
Error handling design
Testing approach and coverage
Overall project organisation

Next step

The next phase is building the SQL writer layer so the pipeline can automatically create tables in SQL Server and populate them with the cleaned data.

Repo

https://github.com/juliana-albertyn/excel-to-sql

I’m learning as I build, so constructive criticism is very welcome.

Small Lessons From Learning Pytest

Juliana Albertyn — Wed, 25 Mar 2026 16:30:23 +0000

I’ve been spending more time with pytest lately, and it’s been surprisingly eye‑opening. Writing tests forced me to look at my own function names with fresh eyes — suddenly 'nullable_rule(...)' didn’t make sense at all, but 'null_allowed_rule(...)' read clearly.

I also bumped into a few pandas quirks along the way: 'bool' and 'boolean' are not the same thing, and 'None' in a Series is definitely not the same as an empty string.

And then there’s parametrisation — such a simple feature, yet it makes tests cleaner, sharper, and far easier to reason about. It’s been a good reminder that testing isn’t just about catching bugs - it’s about understanding your code more deeply.

What small testing discovery has surprised you lately?

A New Adventure in Code

Juliana Albertyn — Sun, 01 Mar 2026 08:09:30 +0000

I retired last year, after more than two decades in a rigid, high-pressure tech environment, and only then did I realise how deep the burnout had gone. For years I came home with nothing left — just enough energy to get through the basics.

Retirement finally gave me space to breathe - but I felt disconnected from the joy of programming. I tried the full-stack route, got as far as CSS, and felt absolutely nothing.

Then I started exploring Python, and something clicked. For the first time in years, I felt curious again. I’ve been learning, building small projects, and rediscovering the part of myself that loves solving problems.

I even renamed my laptops: the old one is Bagend, and the new one is New Adventure. It feels right. Freelancing later in life is challenging, but this is the start of my new adventure — and it feels good to finally have one.