Pivoting to Data Analytics: My Learning Journey & Tech Stack

Pivoting from writing software to analyzing raw data is both exciting and challenging. In this post, I want to share how my personal transition to data analytics is going, the core skills I have built, and the exciting projects I am planning next.

What This Article Covers

• Why I decided to pivot from full-stack engineering to analytics
• How SQL became my absolute strongest skill in this new domain
• Python learning and my concept for a CLI analytics automation tool
• Getting comfortable with Power BI and Microsoft Excel
• My career goals for August and September 2026

Why the Transition to Data Analytics?

As a full-stack engineer, I loved building SaaS applications. However, over time, I noticed that the most satisfying part of my work wasn't just constructing database tables—it was analyzing what was inside them.

I fell in love with finding hidden patterns, designing dashboards, and helping businesses make smart, data-driven decisions. That realization kickstarted my dedication to data analytics.

SQL: The Ultimate Core Pillar

If there is one skill that has become my absolute superpower, it is SQL. Coming from a backend background, I was already familiar with basic queries, but diving deep into database analysis took things to a whole new level.

I've mastered window functions, CTEs (Common Table Expressions), and complex joins to clean, filter, and aggregate huge datasets efficiently. Here is a query pattern I write frequently to analyze cohort retention:

WITH user_cohorts AS (
    SELECT 
        user_id, 
        DATE_TRUNC('month', created_at) AS cohort_month
    FROM users
),
first_orders AS (
    SELECT 
        user_id, 
        MIN(order_date) AS first_order_date
    FROM orders
    GROUP BY user_id
)
SELECT 
    c.cohort_month,
    COUNT(DISTINCT c.user_id) AS total_users,
    COUNT(DISTINCT fo.user_id) AS activated_users
FROM user_cohorts c
LEFT JOIN first_orders fo ON c.user_id = fo.user_id
GROUP BY 1
ORDER BY 1;

[!IMPORTANT] Query optimization is key. Always index columns used in JOIN and WHERE clauses to avoid sluggish dashboards and performance bottlenecks.

Python & The Idea for a Data CLI Automation Tool

Right now, my main focus is mastering Python and its powerful scientific libraries like Pandas, NumPy, Seaborn, and Matplotlib.

Because I love building helper programs for developers under my brand Devbuilds, I am working on an exciting project idea: a CLI-based analytics automation tool.

This CLI tool will allow analysts to instantly run exploratory data analysis (EDA), generate clean visualizations, and export summary statistics right from their terminal using simple commands. I can't wait to build and share it with the community!

Writing custom Python scripts to automate data preprocessing and Exploratory Data Analysis.

Getting Cozy with Excel and Power BI

Admittedly, transitioning to GUI-heavy tools like Microsoft Excel and Power BI felt a little strange at first. As a developer, I am used to writing raw code rather than using visual interfaces.

However, as I spent more time building dashboards, writing DAX formulas, and playing with Pivot Tables, I started getting very comfortable and familiar with them. The ability to quickly draft an interactive chart in Excel or set up dynamic filters in Power BI is incredibly satisfying.

Looking Ahead to August-September 2026

My transition is progressing rapidly, and I am currently working on freelance dashboard design while actively applying for roles.

I am hoping to secure my first official full-time role as a Data Analyst by August-September 2026. I'm eager to bring my unique blend of software automation, database optimization, and data-driven storytelling to a collaborative product team.

If you want to collaborate on a data project or discuss opportunities, check out my Portfolio Projects or reach out to me directly on LinkedIn!