A Day in the Life of a Data Analyst with Kshira Saagar

21 September 2021Written by Emma Woodward
Explore the day-to-day tasks of a data analyst, including the Extract, Transform, Load (ETL/ELT) process, and understand the significance of data storage methods such as databases, data warehouses, data lakes, and data lake houses. Learn from Kshira Saagar's career advice on coding skills, scientific thinking, unlearning, soft skills, and the importance of projects to showcase your skills. Discover how data can be utilised to improve people's lives and explore opportunities in the field of data analysis. Attend a Coder Academy info session to explore the world of data careers further.

Recently, Coder Academy spoke with Kshira Saagar, the chief data officer or “head of data everything” at Latitude Financial Services. They’re the largest lender outside of the big four banks in Australia, making Kshira’s work in data science extremely important for the company.

With a rich and varied career in data science, Kshira had some excellent advice for anyone seeking a career as a data analyst. Let’s start with his breakdown of ETL or ELT.

A Day in the Life of a Data Analyst: Extract, Transform, Load 

One of the key tasks for those working in data science will be to extract, transform, and load (ETL), or extract, load, and transform (ELT) large amounts of data. Companies, government bodies, and individuals have increasingly vast amounts of stored data in a range of databases. While this data is useful for powering individual applications, if collected and analysed it can have a far greater impact, and Kshira believes this impact should be to make people’s lives better.


This step involves extracting data from the system that has collected it, or the system in which it is currently stored.


Now the data must be transformed into a usable form. Different users will look for the metrics and fields that meet their needs.


When the data is ready to load, information can be brought from multiple sources and put into one place.

There are several ways to store data.


Most of the data that analysts work with will come from a database, before going through the ETL process, and being transferred to a larger system.

Data Warehouse 

Just as physical goods are stored in a warehouse space, data that has been extracted from multiple sources can be transformed and loaded into a single data warehouse.

Data Lake

Unlike the orderly warehouse, a data lake is full of data that has been extracted and loaded, but not transformed. With so much data out there, you can save a lot of time by skipping the transform step. It’s a dump of raw data, for someone else to transform according to their needs at a later stage.

Data Lake House 

Just like the name suggests, a data lake house combines elements of a data warehouse and a data lake. You might make some small transformations when transferring data to a data lake house. These won’t be the complete transformations you would make for a data warehouse, but you are setting it aside with some structure.

Now onto some key takeaways from Kshira’s talk.

Kshira Saagar’s Advice for Your Career as a Data Analyst:

1. Learn to Code

You can still work in data without learning to code, but you will be given the grunt work. If you learn to code (in any language) then you can work on more interesting tasks.

2. Learn the Concepts of Coding / Think Scientifically  

Learn coding constructs, not just the particular coding language. This will help you to switch more easily between different languages when needed. Think about why you are doing something, and why it works in a particular way.

Learn to really apply the science of data science. You should never try to torture an answer out of the data that isn’t there. First, come up with a hypothesis. If you want to know why your business is losing new customers, then think about reasons this could be happening. Formulate some hypotheses, and then use the data to test your ten best hypotheses. When you take this approach, you should also be able to remove your own bias, or, at the very least, to be able to clearly see any bias or preconceived assumptions.

3. You Must Unlearn What You Have Learnt

Once you have learnt coding and different methods of data analysis, be prepared to forget everything you know, and learn something new. Rather than becoming a specialist in one area and staying comfortably there, commit to continual learning.

Kshira is at the top of his game, and he still worries that if he doesn’t upskill, he will become a dinosaur. So how does he go on learning new things? He says one way is to never be afraid of finding and learning from people who are smarter than he is. Kshira also likes to make every second Friday a learning day (or half-day) for his team. And finally, he believes you can enhance learning by gamifying it. By making learning something to be prized (perhaps with a literal prize) you will encourage the right atmosphere.

4. Don’t Forget About Soft Skills

Being a data analyst will require soft skills to enhance your technical knowledge.

If you are working in a management role, then you will need to become a data translator. You can bridge the gap between those in your company who are immersed in the world of data science, and those who are not. Anyone can look at a graph and see that the graph is going up, but you can understand the work of the data team, and explain to others in the business, why you think the graph is going up.

Maybe you’re not at management level yet. If you’re still at the entry level of your career, then learn about the business that you’re in, not just the role you fill. Appreciate the business and the sector, and learn to contribute meaningfully to it.

Finally, anyone at any level can be a mentor, and you should pass on what you have learnt both amongst your team, and to others.

5. The Importance of Projects / Keeping Your Skills Going Beyond the Course

If you’re coming to the end of a bootcamp or short course, then you’re probably thinking about your potential job outcomes. But Kshira was keen to stress that you don’t need to wait for employment to use the skills you have learnt in the course. In fact, taking on a project can be a fantastic way to showcase your skills to potential employers.

Projects help you to keep sharpening your skills. They stop the rust. Projects also don’t have to be perfect. You could write some code to automate a mundane task. You could also search for open datasets. There are many that are freely available, including data made available by Australian government agencies. These could allow you to work on all sorts of things, and you are sure to find community organisations and others who would be grateful for your skills and time.

If you have come from a different field and you are transitioning into data science, then any projects you work on provide proof that you know what you’re talking about. This can break the hiring bias when you apply for jobs and potential employers see unrelated previous experience on your resume. It is also a form of personal branding. Start building a portfolio on GitHub, and embrace taking on new projects as part of your lifelong learning.

Kshira believes you can solve anything with data. If you ever find yourself thinking, “There must be a better way to do this”, then see if you can turn it into a project and solve the problem with data.

6. Use Data as a Tool to Make People’s Lives Better

Kshira talks about taking his experience of working with data in fashion (at The Iconic) to working with data in finance. He believes the transition was easy because retail is all about making the customer experience better, and in his current job, his role remains to use data to make people’s lives better.

Now, most of Kshira’s time is devoted to serving internal customers (the employees of the company). Kshira believes everyone’s life could be made easier with the right data analysis and automation. All workers face the challenge of repeated actions that take up significant portions of time each day. Every business requires vital data regarding their revenue, their customer’s requirements and satisfaction with their products or services.

The goal of data analysis should be to make life easier. Whether you’re saving time by writing the code that automates repeated tasks, or developing a system that gives you the information you need at a glance, analysing and structuring data allows you to leave the tedious tasks behind, and to work smarter, not harder.

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