How to build your career in Data and Analytics?

I have been working in data and analytics for more than 11 years now and during that period, “How can I become a BI engineer” is a question I have heard more often than not. Especially fresh graduates want to know how to start their career as a BI Engineer, a Data Engineer, a Data Scientist, etc. I have seen lots of blogposts, sessions covering this topic. The answer to this question is subjective. There is a possibility that different people might provide answers for this. In this post, I want to share my version of the answer based on how I became part of BI and Analytics world.

Learn SQL

I still believe that SQL as one of the main components of BI space. When a person is working with data, there are so many languages they can/ must use. However, out of all those languages, SQL is still the king. Back then and even now, all the computer graduate programmes have a course for SQL. SQL has variations based on the vendor. However, in almost all the cases, core concepts remain the same. If you are a undergraduate, pass the SQL modules in flying colours. If you are fresh graduate or someone who is new to SQL, there are lots of SQL books, courses available to go in depth of SQL programming. What you learn from graduate programme is good starting point. But deep dive to SQL is a must.

Understand Data Warehouse Concepts

If you want to be successful in BI world, learning DW concepts is a must. I know some people think that Data warehousing is dead now and it’s all about data lake and lakehouse. I don’t think that is true, data warehousing is very much alive and all the DW concepts are still valid. I have seen people still ask DW related questions such as different fact types and SCD implementations in interviews. I learnt these concepts by reading all the books by Ralph Kimball and I still recommend those books to people who ask me about how to learn BI. Even if you don’t do DW development, knowing DW concepts is really useful when you use tools such as Power BI.

Pick your role

In a data and analytics project, there are various roles. While the most common role is data engineer, all the other roles have equal importance in a project. Business Analysist, Data analysists, Data Scientist, AI Engineer, QA Engineer and DataOps Engineer are some of these. You don’t necessarily have to be DW developer or Data Engineer to be successful in data and analytics world. Pick a role you feel suites and follow that path. However, there are scenarios where one has to wear all these different hats in a project as well. Therefore, always good to know what responsibilities and tools are used by each job role.

Select a primary platform to work on

When you want to be in BI domain, you will always have to pick a platform to be an expert. I have seen very few people who are expert in more than one domain. But that is once you have established your presence in the industry. There are so many vendors with considerable presence in the BI world. Microsoft, AWS, Google, Oracle, Tableau, Snowflake and Qlik can be considered as some of the leaders among them. Obviously, knowing their offerings within BI domain is important. But you have to pick one platform and study in depth on that to become an platform expert.

Even within a platform, there are so many technologies related to data and analytics. For an example, in Microsoft BI platform, there are experts for each tool such as Power BI, Azure Data Factory, Azure Databricks, Synapse, etc. Personally, I wouldn’t recommend someone focusing only on one tool. I believe that is too narrow. However, there are so many how focus on one tool and become an expert in it. There can be a preferred tool within a platform to you. Nevertheless, since data analytics domain is so vast, being an expert on a tool could open some opportunities for you.

Certifications

Each analytical platform, vendors have certifications. These certifications are so important to gain in-depth knowledge about those tools and platform. When you have these certifications under you, when you go to an interview, the interviewer knows that you have a good understand and knowledge about the particular tool/ platform. For me, that could be important and relevant to the job than your degree.

When you do a certification, just don’t do any certification. Most of these certifications are designed to for different roles. For an example, some certifications are targeted for data engineer role, some are for data scientise role, some are for administrator role. Pick the right certification path based on the role you decide you want to be.

Social media

Social media is a double-edged sword. If you use it correctly, it connects you with right people and provide lots of opportunities. Create a LinkedIn profile and a twitter account. Connect with technology evangelist who share the knowledge your preferred platform. There are so many people who speaks about, write about their preferred tool/platform. Talk to them, follow them, ask questions. You will be surprised how helpful these people are.

Keep yourself up to date

Obviously, Keeping yourself up to date is very important in technology industry. However, data and analytic world is changing for fast compared to any other domain I know. Areas such as Artificial Intelligence, data science are still in very early stage and hence, changes happen very frequently. Being up to date on all the changes are very hard. However, in your preferred platform, keeping yourself up to date on your preferred tool is very important. Read blogposts, watch YouTube videos, keep your certifications up to date, participate technical meetups.

I believe this will help for people who wants to start their career in data and analytics world. Thanks for reading. Stay safe! Cheers!