When I first studied Data Analytics and Data Science, I imagined my days would be filled with creating forecasts, running models, and predicting future behaviours. But in practice, I discovered that the role of a Data Analyst is much broader, especially when working with organisations at different stages of data maturity.

Why Data Engineering Skills Matter

In today’s world, being “cloud ready” is not optional. Analysts are expected to understand aspects of Data Engineering, from data integrity and governance to building pipelines that prepare information for meaningful analysis.

Having these skills means you’re not just interpreting data, but also making sure the data foundation is solid. This is vital for organisations where systems are still being connected and processes are evolving.

Where to Start Your Data Engineering Journey

The best way to grow your Data Engineering skills is to get hands-on:

• Learn how raw data becomes analysis-ready.
• Practise creating ETL (Extract, Transform, Load) processes.
• Experiment with different sources: Excel files, SharePoint lists, SQL databases, APIs, and more.

Every time you work with a new source, you expose yourself to a different environment. This builds resilience and flexibility—two traits that make you a better fit across industries and organisations.

My Favourite Tools for Data Engineering

Here are some tools I personally recommend as a starting point:

• Google BigQuery – for scalable cloud-based analytics.
• Prefect – for workflow orchestration and automation.
• Python – the go-to language for transformation and ETL.
• SQL Stored Procedures – for powerful in-database operations.
• SSIS – for structured ETL workflows within Microsoft environments.

Once you’ve mastered these, you can explore more advanced platforms like Snowflake, DBT, and Databricks.

And here’s a practical challenge: if you’ve got an old computer lying around, install Linux, play with Python, and practise creating simple pipelines. You’ll learn more than you expect from the process.

Embracing Multiple Hats

The modern Data Analyst is part analyst, part engineer, part storyteller—and now, increasingly, part AI collaborator. Wearing multiple hats is no longer a choice, it’s a requirement. The good news is that with the right tools, practice, and mindset, you’ll be well prepared to thrive.

Next Steps

At Excel in BI, we help professionals and organisations build these skills in practical, approachable ways.

• Connect with our webinars to see these concepts in action. http://www.excelinbi.com/webinars
• Check out our courses to grow your skills step by step. http://www.excelinbi.com/courses

The journey of becoming a versatile Data Analyst starts now. You’ve got this!