With over five years of experience in web design and development, I create stunning and responsive websites for clients across various industries and niches. I work as a Web Designer and Developer at Webflow, a leading no-code platform that empowers anyone to build and launch websites without writing code. I also offer my SEO expertise as a freelancer on Fiverr, a global marketplace that connects businesses with digital services.

I have mastered multiple tools and platforms, such as HTML, CSS, JavaScript, WordPress, Shopify, and Squarespace, and I have a passion for creating beautiful and user-friendly web pages that showcase the brand identity and value proposition of my clients. I have successfully completed hundreds of projects, ranging from landing pages and portfolios to e-commerce sites and blogs, and I have received multiple positive reviews and ratings from satisfied customers. I am always eager to learn new skills and trends in web design and development, and I am constantly updating my portfolio and profile to reflect my latest work. I am a reliable, creative, and collaborative web designer and developer, and I strive to deliver high-quality and satisfying results for my clients.

Sarafatul Rabby Ⓥ

Most web developers think landing high-paying clients is about luck or having a big network.

It’s not.

It’s about how you communicate your value.

I used to send the same generic message: "Need help with your website?" 
Got ignored, every time.

Until I changed my approach.

Now I record a 2-minute Loom video.

I show them exactly where their site is losing money 
and how I’d fix it.

The response rate? Jumped from 5% to 67%.

What’s the difference? 
It’s confidence.

Instead of hoping they’ll say yes, I’ve already shown I understand their problem. 
I’ve provided real value upfront.

A quick video beats a cold DM any day.

Most people won’t do it. 
It’s uncomfortable, takes effort, and feels risky.

But that’s where your edge is.

While everyone else sends the same tired pitch, 
you’re demonstrating real understanding.

It’s not magic. 
It’s just a simple shift in how you present yourself.

One video today can turn a cold lead into your next client.

And that’s the power of showing up differently.

Sarafatul Rabby

Most people see frontend and backend as two sides of the same coin.

But the truth? They’re more like two different worlds.

Frontend feels like the face of the business. 
It’s what users see, click, and interact with. 
It’s designed to make an impression, to engage.

Backend? It’s the engine under the hood. 
It handles all the heavy lifting—data, logic, servers. 
Most users never see it, but without it, the frontend is just a pretty picture.

People often chase the shiny frontend. 
They want to craft beautiful interfaces, animations, slick layouts. 
That’s important, but it’s just half the story.

The backend is where the real power lives. 
It’s where your app actually runs, where the data lives, and where the magic happens.

Too many focus on one side. 
They think frontend is ‘easier’ or more glamorous. 
But a beautiful interface without solid backend is like a fancy car with no engine. 
It looks great, but it doesn’t go anywhere.

Understanding both? That’s the real skill. 
It’s what separates a good developer from a true builder.

And here’s the punch: 
You can’t build something sustainable with just frontend tricks. 
You need the backbone that keeps everything running smoothly.

So, don’t just chase the fancy UI. 
Master the backend, too.

That’s where the real value hides.

Most web APIs are bloated with simple mistakes that kill performance.

They’re often invisible to most devs, especially juniors and mid-levels.

Here’s a truth most senior devs know: 
Small tweaks can make a huge difference.

Like preferring async/await for all I/O. 
Or adding the right indexes in your database. 
Using caching, response compression, or even minimal APIs instead of heavy controllers.

It’s not about rewriting everything. 
It’s about knowing where to look first.

Sometimes, a simple change — turning on HTTP/2 or adding a cache — can cut response times in half.

Performance isn’t just about hardware. 
It’s about knowing the tiny details that add up.

Most devs overlook these. 
But the best? They’re always hunting for that hidden bottleneck.

You don’t need to be a senior to start spotting them.

Just ask yourself: 
What’s eating my API’s speed right now? 
And fix that first.

Because in web development, small edges win big battles.

Most people think coding is about memorizing syntax or stacking up tech stacks. 

It’s not. 

It’s about solving problems, one line at a time. 

When I started, I dove into tutorials—W3Schools, freeCodeCamp, Udemy. 

I thought that was enough. 

It wasn’t. 

Real progress came when I stopped chasing the next shiny resource and started building. 

I picked a project, broke it down, and just kept moving forward. 

Node.js, React, jQuery—those came later. 

First, I learned to think like a developer. 

Code isn’t magic. 

It’s persistence. 

It’s patience. 

And a willingness to get your hands dirty. 

You won’t become a master overnight. 

But if you keep showing up, piece by piece, you’ll see the pattern. 

The key isn’t the tools. 

It’s your mindset. 

The code will come when you focus on solving real problems, not just ticking tech checkboxes. 

Stop waiting for the perfect moment. 

Start building with what you have today. 

Because that’s how you turn lines of code into real skills.

Another project wrapped up. 

This time, a website built from scratch that combines clean design with real speed. 

No fluff. Just a layout that’s fully responsive, easy to navigate, and optimized for performance. 

I don’t just make it look good—I make sure it works hard for the client’s goals. 

Every element is crafted to reflect their brand, their message, their vision. 

It’s not about trendy bells and whistles. It’s about delivering results that matter. 

If you’re thinking of a website refresh or a new build that actually moves the needle, let’s talk. 

Because a website isn’t just an online brochure. It’s a tool. 

And when it’s done right, it can change everything.

Think most developer interviews are about memorizing buzzwords? 

That’s how most people approach it. They binge lists of “top 50 interview questions,” cram syntax, and hope for the best. The problem: those questions rarely show how you actually think.

I don’t care if you can recite the difference between let, var, and const. I want to see you solve real problems on your feet. Understand how the pieces fit together. Handle the curveballs that real projects throw at you.

Here’s what actually matters:

Can you take a messy UI and make it usable? 
Can you spot the bottleneck when a page load drags past 3 seconds? 
Can you explain why that one CSS fix broke half the layout? 
Can you debug async code without getting lost in console logs? 
Can you build something practical, not just textbook-perfect?

You don’t need a 100-question checklist to prep for interviews. You need to get your hands dirty. Build, break, fix, repeat.

The best preparation isn’t in the notes everyone shares. It’s in the work no one else wants to do.

If you want to stand out, stop aiming for “right answers.” Start showing how you think.

That’s what gets you hired.

Most companies guard their best knowledge like it’s a trade secret.

But here’s a twist—Webflow just put a full AEO course online. Free. No paywall, no endless email funnels.

AEO isn’t some buzzword. It’s the next big territory for anyone building on the web. AI search is only getting more dominant, and if your site isn’t ready for it, you’ll get left behind.

Most folks still treat SEO as the end game. It isn’t anymore. AEO is a different beast—new rules, new tactics, new kinds of visibility.

What I like here: they didn’t just explain what AEO is. They broke down the how. Schema markup, llms.txt, and the technical steps you actually need to take to get cited by AI models.

No fluff, no vague tips. Concrete, practical, actionable.

It’s rare to see a platform walk its talk like this. They’re not just selling features—they’re teaching the future.

Most won’t pay attention until it’s too late.

But the ones who adapt early? They’ll have the edge when everyone else is still playing catch up.

It’s not about chasing the latest trend. It’s about being ready for what’s coming next.

Dr Emmanuel Ogungbemi

Head of Data Engineering | I help you improve your productivity with data and AI

From being a data analyst to leading the development of data pipelines, my journey in data engineering and analytics has been both challenging and rewarding.

When I began my career in data, I faced the daunting task of turning vast amounts of raw data into actionable insights.

The problem? Many organisations struggle with fragmented data and inefficient processes.

Here's how I tackled these challenges and built a successful career in data:

1. Crafted Data Solutions: Designed and implemented a Common Data Platform, ensuring seamless data integration and access.
2. Led Innovative Projects: At NATS, I contributed to the world's first AI system for air traffic control, utilising digital twinning and machine learning.
3. Developed Future Talent: Shaped data science and engineering education at Multiverse by developing comprehensive curricula for degree-level apprenticeships.
4. Consulted on High-Impact Projects: Provided AI and data science consultancy for industry giants like BP, Sword and Chaucer (now BIP UK), driving data-driven decision-making and innovation.

Now, I'd like to talk about you.

Are you aspiring to become a successful data analyst/data scientist/data engineer but need help figuring out where to start?

Here's a roadmap to guide you:

1. Master the Fundamentals:
- Learn key programming languages like Python and SQL.
- Understand data structures, algorithms, and database management.

2. Gain Practical Experience:
- Work on real-world projects to apply your skills.
- Use tools like Power BI and Tableau for data visualisation.

3. Join the Community:
- Participate in data science forums and attend industry events.
- Network with professionals to exchange knowledge and opportunities.

4. Continuous Learning:
- Stay updated with the latest trends and technologies in data analytics.
- Take advanced courses to deepen your expertise.

To help you get started, I've created a comprehensive Data Analyst Roadmap that outlines the essential steps and resources you need to succeed.

Join my email list to register your interest in my free Data Analyst Roadmap.

You have the potential to excel in the data industry, and I'm here to support your journey.

Let's connect and share our experiences in data engineering and analytics!

Head of Data Engineering

I'm excited to share an invaluable resource: the Data Engineer Interview Guide by Simplilearn!

Preparing for a data engineering interview can be daunting, but this comprehensive guide has got you covered. 

Whether you are a fresh graduate or an experienced professional, this document provides a detailed overview of the key questions and answers you can expect in a data engineering interview.

Key topics include:
- Understanding the role and responsibilities of a data engineer
- Differences between data engineers and data scientists
- Key skills required for data engineering, including SQL, Hadoop, and Python
- Essential frameworks and applications for data engineers
- Commonly asked interview questions and how to answer them
- Practical tips for handling job-related crises and real-world scenarios
- Insights into data modelling, ETL processes, and working with big data in cloud environments


Enhance your interview preparation and stand out from the competition with this detailed guide!

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- Save to revisit down the road
- Add your comment!

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Talks about #data, #kindness, #interviewtips, #selfimprovement, and #careertransition

Data Engineering Intro

I help you break into data science and AI with practical tips, real-world insights, and the latest trends.

SQL Introduction Handnotes

The real secret to becoming a data professional isn't technical skills—it's storytelling.

Here's how narrative trumps numbers.

When I first entered the world of data, I was captivated by the endless possibilities that numbers could unlock.

But soon, I realised something crucial: data alone doesn't speak.

It's the story you weave around it that truly makes an impact.

Let me explain how you can harness the power of storytelling in data:

1. Understand Your Audience
  ↳ Tailor your narrative to resonate with the specific needs and interests of your audience.
  Whether you're speaking to executives or data enthusiasts, adjust your story accordingly.

2. Find the Human Element
  ↳ Data is ultimately about people.
  By highlighting the human impact, you make your insights relatable and actionable.

3. Create a Compelling Narrative Arc
  ↳ Like any good story, your data-driven narrative should have a clear beginning, middle, and end.
  Start with the problem, introduce the data, and conclude with actionable insights.

4. Simplify Complex Data
  ↳ Great storytelling involves breaking down complex data into understandable and engaging segments.
  Use visuals and analogies to make your points clear.

5. Inspire Action
  ↳ Your story should lead to a call to action, encouraging your audience to make informed decisions based on your insights.
  
Remember, data storytelling isn't just about presenting facts—it's about driving change.

By mastering the art of storytelling in data, you transform from a mere analyst to an influential data storyteller.

And the best part?

Anyone can learn this skill.

So, are you ready to craft stories that drive data-driven decisions?

Start weaving your narrative today.

Excited to share an insightful resource: MLlib: Scalable Machine Learning on Spark by Xiangrui Meng and collaborators!

This comprehensive guide explores the powerful capabilities of MLlib, Spark's scalable machine learning library.

It covers everything from basic algorithms to advanced machine learning techniques, making it an essential resource for data scientists and engineers looking to leverage Spark for large-scale data processing and analysis.

Key topics include:
- Overview of MLlib and its integration with Apache Spark
- Machine learning algorithms: classification, regression, clustering, and collaborative filtering
- Practical examples and implementations of logistic regression, k-means clustering, and ALS
- Performance comparisons and scaling properties of MLlib
- New features and updates in MLlib v1.x
- Integration with other Spark components like Spark SQL, Streaming, and GraphX


Unlock the full potential of machine learning with MLlib on Spark and take your data projects to the next level!

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- Save to revisit down the road
- Add your comment!

Why I believe the future of data engineering lies not in new tech but in teaching empathy and communication.

I started in data engineering with the belief that the latest tech would solve every problem.

That mindset shifted.

Here's why: it's not just about the tools.

It's about understanding the humans behind the data.

In my journey from data analyst to data engineering leader, I've learned that the hardest problems aren't technical.

They're human.

When I was tasked with transforming raw data into insights, I quickly realised that many organisations were drowning in fragmented data.

Not because they lacked technology, but because they lacked communication.

→ Data was siloed.

→ Teams didn't talk to each other.

→ Processes were inefficient.

So I focused on:

1. Building Bridges: I brought together diverse teams, ensuring everyone understood the data's value and purpose.

2. Teaching Empathy: I encouraged my team to see data not just as numbers but as stories with human impact.

3. Enhancing Communication: I led efforts to develop clear channels and languages for data discussions, making complex insights accessible to all.

It wasn’t just about algorithms.

It was about collaboration.

About understanding the fears and hopes of those who rely on the system.

When we embrace empathy and communication, we transform data from a static resource into a dynamic force for change.

We give it life.

The future of data engineering won't be about the flashiest tools.

It will be about the human touch.

So here's my challenge to you:

Focus on the human side of data.

Ask more questions.

Listen more.

Let's build a future where data brings us together, not keeps us apart.

What do you think?

How can we make data engineering more human?

Share your thoughts below.

taplio

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Sarafatul Rabby Ⓥ

Dr Emmanuel Ogungbemi

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taplio

The Battle

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Dr Emmanuel Ogungbemi

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