You’re a data engineer who wants to switch to data analysis. What’s the best way to make that happen?

In my opinion, this change requires acquiring specific skills and the ability to analyze and derive insights from data. Some steps can ease this transition, such as: Research and understand the key responsibilities and skills required for a data analyst role, include proficiency in statistical analysis, data visualization, data cleaning, and domain-specific knowledge, Enhance your statistical knowledge, as data analysts often use statistical methods to analyze data and draw conclusions and Stay up to date with industry trends, tools and techniques in data analysis.

Data analysis is like creative writing, like in The more you analyse thru projects, the more you learn. Learning curve would be good for Data Engg , cheers!

Some of the best Business Intelligence (BI) tools that are widely used in the industry and that I also love to work with them, include Tableau for its interactive data visualization capabilities, Power BI from Microsoft for its integration with Azure and Office 365, Looker for its data exploration, and Qlik Sense for its associative analytics engine. Each tool has its unique strengths, so the best choice depends on your specific needs, such as data sources, complexity of analysis, and visualization requirements.

1.Understand the Role 2.Acquire Relevant Skills 3.Gain Practical Experience 4.Build a Portfolio 5.Tailor Your Resume and LinkedIn Profile

When building your data analysis portfolio, consider showcasing a variety of projects that demonstrate your skills across different aspects of data analysis, including data cleaning, visualization, and predictive modeling. Choose projects that reflect real-world problems and your ability to derive actionable insights. Use tools like Jupyter Notebooks for coding projects and Tableau or Power BI for visualizations to make your work accessible. Remember to document your thought process, methodologies, and the impact of your findings. Lastly, ensure your portfolio is easily accessible online, through platforms like GitHub or a personal blog, and clearly communicates your capabilities and achievements to potential employers.

As said, showcasing Portfolio is the key, as a example you can analyze your profile and pitch your skills. I believe that would be best example.

Also follow industry people, and look the events and people they are following. Also reach out to them, what would be the essential skills to develop in the first 3 months and proficient skills in the next 6-12 months

Pitch your portfolio in 30 seconds, as the attention span is less for recruiters as they see many number of profiles. How can you do that??? Sending your portfolio in a image instead of resume?

To transition from data engineering to data analysis, focus on developing your analytical skills and gaining experience with data analysis tools and techniques. Learn data visualization using tools like Power BI, Tableau. Practice by working on data analysis projects, either independently or as part of a team. Additionally, consider taking online courses or certifications in data analysis to deepen your knowledge. Networking with data analysts and participating in relevant communities or forums can also provide valuable insights and opportunities for growth in this field.

In future, the difference between Data engg. and data analysis would decrease further. But there would be still some roles who want you to specialize in a particular area only.