Shakra Shamim Interview
“From cracking 50+ interviews to simplifying analytics for thousands online, Shakra Shamim proves that clarity—not complexity—is what drives real success in data.”
Q. You’re balancing a high-impact role at Amazon/Myntra and a content creator identity. What was your path into data, and when did you decide to teach others along the way?
When I started my data analyst journey about three and a half years ago, I was honestly lost. There was no single clear roadmap for beginners; everyone had their own version of advice. Some said start with Python, others swore by SQL or Power BI.I tried to follow everything, but it only made things more confusing. Fast forward a few years to my time at Myntra. By then, I had become a more confident analyst, but I still remembered that same confusion I once had.
That’s when I decided to create a 100-day roadmap for aspiring analysts—something simple, structured, and practical. I posted it on LinkedIn and discussed it in my 1:1 sessions, and the response was incredible. Balancing both roles wasn’t easy.
That’s when I decided to create a 100-day roadmap for aspiring analysts—something simple, structured, and practical. I posted it on LinkedIn and discussed it in my 1:1 sessions, and the response was incredible. Balancing both roles wasn’t easy.
I would record or plan content late at night or on weekends. But over time, I realised both worlds complement each other. My full-time work exposes me to real data challenges, and content creation lets me turn those experiences into lessons others can actually use.
For me, every idea begins with data and insights. Numbers might look simple, but if you pay attention, they quietly tell you where things are slipping and what needs to be fixed.
Q. In a large e-commerce setting, how do you turn raw data into business action? Walk us through one project where you made a measurable impact.
For me, every idea begins with data and insights. Numbers might look simple, but if you pay attention, they quietly tell you where things are slipping and what needs to be fixed.In e-commerce, we deal with millions of rows of data every single day - from customer interactions and delivery timelines to workforce performance metrics. I always start by diving into this data to find stories behind the numbers and patterns that reveal where improvement is needed. During one such analysis, I noticed a clear imbalance: some teams were constantly overloaded while others had idle time.
On normal days, this went unnoticed, but during major sale events like the Great Indian Festival or End of Reason Sale, this imbalance hit hard. Customer requests increased nearly 4-5 times, response time shot up, and even a small mismatch in workforce allocation led to longer wait times, delayed resolutions, and higher escalation rates.
On normal days, this went unnoticed, but during major sale events like the Great Indian Festival or End of Reason Sale, this imbalance hit hard. Customer requests increased nearly 4-5 times, response time shot up, and even a small mismatch in workforce allocation led to longer wait times, delayed resolutions, and higher escalation rates.
Managers were firefighting throughout the day, manually moving hundreds of associates between queues just to keep things running. It was a huge operational bottleneck hiding in plain sight, something only data could reveal clearly.
That's when I decided to act on the insight and built a Python-based bot to automate this entire workforce allocation process. Now, managers simply raise a ticket for their CSAs, and the bot handles everything automatically. It checks eligibility, validates rules, and looks for available capacity. If all conditions are met, it assigns the group to the correct domain, such as returns, payments, or delivery issues.
That's when I decided to act on the insight and built a Python-based bot to automate this entire workforce allocation process. Now, managers simply raise a ticket for their CSAs, and the bot handles everything automatically. It checks eligibility, validates rules, and looks for available capacity. If all conditions are met, it assigns the group to the correct domain, such as returns, payments, or delivery issues.
The result was game-changing: what earlier took hours now happens in under two minutes, reducing manual effort by nearly 90%, preventing misallocations, and allowing teams to handle the busiest sale periods without chaos. That's how I see data turning into business action not just identifying patterns, but transforming those insights into something that makes large-scale operations smoother, faster, and smarter.
In my early days, I used to focus only on technical perfection, writing optimised queries, building dashboards, and automating reports. But over time, I realised something important: insights don't drive change, stories do. When you explain data with context, for example, not just saying "our returns have increased by 7%" but adding "that means nearly 7 out of every 100 customers are not happy with their purchase experience", people instantly connect to it.
Decision-makers don't remember the percentage; they remember the story behind it. Storytelling isn't about fancy visuals or emotional talk it's about empathy. It's about understanding what matters to your audience and framing the data in a way that helps them act on it. So yes, technical skills make you efficient, but storytelling makes you valuable. It's the one skill that turns an analyst into a trusted business partner someone who doesn't just present numbers but helps shape decisions.
One rejection that truly changed the way I looked at analytics was from EXL, during my switch from Cognizant to a Data Analyst role. I had cleared both technical rounds, everything from SQL, Excel to Python went perfectly. But in the final round with the VP, things took a turn.
Q. Between SQL, Python, Power BI, and storytelling, which skill truly differentiates an average analyst from a great one?
If I have to pick one, I'd say storytelling because that's where analysis truly turns into impact. Almost every analyst today knows SQL, Python, and Power BI. These tools help you fetch data, clean it, and visualise it beautifully. But what separates a great analyst is the ability to look beyond the charts and numbers to understand what the data is trying to say and then communicate it in a way that makes others care.In my early days, I used to focus only on technical perfection, writing optimised queries, building dashboards, and automating reports. But over time, I realised something important: insights don't drive change, stories do. When you explain data with context, for example, not just saying "our returns have increased by 7%" but adding "that means nearly 7 out of every 100 customers are not happy with their purchase experience", people instantly connect to it.
Decision-makers don't remember the percentage; they remember the story behind it. Storytelling isn't about fancy visuals or emotional talk it's about empathy. It's about understanding what matters to your audience and framing the data in a way that helps them act on it. So yes, technical skills make you efficient, but storytelling makes you valuable. It's the one skill that turns an analyst into a trusted business partner someone who doesn't just present numbers but helps shape decisions.
Q. You cracked 50+ interviews before securing roles. Which rejection taught you the most, and how does that story shape your content now?
One rejection that truly changed the way I looked at analytics was from EXL, during my switch from Cognizant to a Data Analyst role. I had cleared both technical rounds, everything from SQL, Excel to Python went perfectly. But in the final round with the VP, things took a turn. They started asking business case studies and guesstimate questions, things like customer churn, retention rate, and revenue impact. And honestly, at that time, I had no idea how to approach those. 1 was good technically, but I couldn't connect my answers to a real business outcome. That interview made me realise something important: being a good analyst isn't just about knowing how to write queries or create dashboards.
It's about understanding how your analysis fits into the bigger business picture. If you can't explain the "why" behind your numbers, even the best SQL won't save you. After that day, I started learning business concepts alongside tools-reading case studies, understanding customer metrics, and building end-to-end projects.
It's about understanding how your analysis fits into the bigger business picture. If you can't explain the "why" behind your numbers, even the best SQL won't save you. After that day, I started learning business concepts alongside tools-reading case studies, understanding customer metrics, and building end-to-end projects.
That shift in mindset completely changed my career. And that experience now shapes everything I teach. In my Bootcamps and YouTube content, I always tell learners don't just learn tools, learn how to think like a business analyst. Because that one rejection at EXL taught me what no course ever could tools get you the interview, but understanding gets you the job.
The most common mistake I see among self-learners is treating learning like a checklist. People jump from one topic to another - SQL today, Python tomorrow, Power BI next week without really understanding how all of it connects.
Q. You advocate self-learning paths for analysts without tech degrees. What common mistake do you see, and which project type best shows real analytical skill on a resume?
The most common mistake I see among self-learners is treating learning like a checklist. People jump from one topic to another - SQL today, Python tomorrow, Power BI next week without really understanding how all of it connects. They complete 4-5 courses, collect certificates, and still feel stuck when it's time to build something real. That's because analytics isn't just about learning tools; it's about solving business problems using data. You can't learn that from tutorials alone you learn it by applying concepts to real scenarios.
If you're building a portfolio or resume project, pick something that shows end-to-end thinking, not just coding. For example, take an e-commerce dataset and answer a real business question like:
Which customer segment has the highest repeat rate?
What’s causing delivery delays?
How can we improve product conversions?
When you do that, you're not just showing that you can write queries - you're showing that you can think like an analyst. That's what recruiters notice. And you don't need a tech degree for that you need curiosity, structure, and consistency. I've seen so many non-technical learners transform their careers just by focusing on practical problem-solving instead of perfection.
If you're building a portfolio or resume project, pick something that shows end-to-end thinking, not just coding. For example, take an e-commerce dataset and answer a real business question like:
Which customer segment has the highest repeat rate?
What’s causing delivery delays?
How can we improve product conversions?
When you do that, you're not just showing that you can write queries - you're showing that you can think like an analyst. That's what recruiters notice. And you don't need a tech degree for that you need curiosity, structure, and consistency. I've seen so many non-technical learners transform their careers just by focusing on practical problem-solving instead of perfection.
Bio
Shakra Shamim is a data and business analyst at Amazon with over four years of experience helping global teams turn raw data into powerful business decisions.
She has previously worked with leading organizations like Myntra and MiQ Digital, where she focused on automation, data strategy, and process optimisation.
Beyond her corporate career, Shakra has become one of India’s most followed data analytics content creators, inspiring over 250K+ followers on Instagram, 190K+ on LinkedIn, and 70K+ on YouTube. Her tutorials, 100-day learning roadmaps, and practical bootcamps have helped thousands of learners break into data analytics without formal tech degrees.
Beyond her corporate career, Shakra has become one of India’s most followed data analytics content creators, inspiring over 250K+ followers on Instagram, 190K+ on LinkedIn, and 70K+ on YouTube. Her tutorials, 100-day learning roadmaps, and practical bootcamps have helped thousands of learners break into data analytics without formal tech degrees.
She believes that data isn’t just numbers—it’s a language that helps businesses understand people. Through her content, she’s simplifying analytics for the next generation of problem-solvers, showing that clarity, empathy, and real-world thinking are what turn good analysts into great ones.
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