🎯 Day 52 of My Data Analytics Journey !

#analytics #career #devjournal

Last week marked another milestone in my Data Analytics learning journey. On Day 52, I took a break from coding to focus on understanding the real-world job roles that exist in the tech industry — especially those related to data.

I spent my time analyzing several job descriptions such as:

🔹 Power BI Developer
🔹 SQL Developer
🔹 Backend Developer
🔹 Data Analyst

Each of these roles sounded similar at first glance, but after going through them carefully, I discovered that each one has unique skills, tools, and responsibilities that make them stand out.

💼 Understanding Each Role

🔹 Power BI Developer

A Power BI Developer is responsible for creating interactive dashboards and visual reports. They connect multiple data sources, transform raw data, and build reports that help organizations make decisions quickly.

Skills required: Power BI, DAX, SQL, Excel, and strong visualization skills.

🔹 SQL Developer

SQL Developers focus on data storage and database management. Their main goal is to write efficient queries that extract useful insights from large datasets.

Skills required: SQL, database design, query optimization, and experience with systems like MySQL or SQL Server.

🔹 Backend Developer

Backend Developers handle the logic and server-side functionality of applications. They manage APIs, databases, and ensure smooth data flow between systems.

Skills required: Python, Java, Node.js, SQL, API handling, and debugging.

🔹 Data Analyst

Data Analysts are problem solvers who collect, clean, and interpret data to support decision-making. They often use tools like Excel, SQL, Python, and visualization tools such as Power BI or Tableau.

Skills required: Data cleaning, statistical analysis, visualization, and business communication.

🗣️ Discussion with My Trainer

I discussed each of these job roles with my trainer. He not only explained the differences clearly but also guided me on how to read job descriptions smartly — focusing on keywords, tools, and expected outcomes.

He also explained how to answer interview questions effectively, such as:

“How do you handle large datasets?”
“Can you explain your data cleaning process?”
“How do you use Power BI for decision-making?”

This discussion helped me gain more confidence in understanding where my current skills fit best and what I need to focus on next.

💡 Key Learning Takeaways

Every job role in data and tech has a specific purpose.
Reading job descriptions helps identify industry expectations.
Interview preparation should focus on skills and problem-solving rather than memorization.
Communication and clarity are just as important as technical skills.