How I got the United Kingdom Global Talent Visa

I recently received United Kingdom Tier-1 Global Talent Visa. I have received lots of messages asking about the visa process and I couldn’t reply  to everyone individually. That’s why I am writing here to share my experience.

What is UK Global Talent Visa?

The Global Talent visa is a type of UK visa offered to leaders or potential leaders in one of three fields: Academia, Arts/Culture, and Digital Technology. It allows the visa holder to live, work, or study in the UK without needing any sponsorship. There are two variations of this visa: the Exceptional Promise Route for potential leaders, and the Exceptional Talent Route for more experienced individuals.

I am a software engineer working in Meta and my expertise falls under Digital Technology. I have been working in this field since 2014, which qualified me for the Exceptional Talent Route of the Global Talent visa.

How did I get the visa?

The application process for this visa is kind of similar to typical university application in US/UK. The initial step involves obtaining an endorsement from Tech Nation, which is the only UK government-approved endorser for this field.

To get the endorsement, you need to meet one mandatory criteria and two of the four optional criteria. The mandatory criteria is the applicant must be “show that they have been recognized as a leading talent in the digital technology sector in the last 5 years.”.

Also there is a list of 4 optional criteria and I had to select two from them. I chose the first (OC1) and the second (OC2) one. For the first one, I need to prove “track record for innovation as a founder or senior executive of a product-led digital technology company or as an employee working on a new digital field or concept”. For the second one, I need to prove I have “proof of recognition for work beyond the applicant’s occupation that contributes to the advancement of the field”.

You need to submit 3 recommendation letters from experts in your field and submit up to 10 pieces of evidence to show how you meet the criteria. Also you need to submit a statement of purpose as well. The amount of paperwork needed can be a bit overwhelming at the beginning.

Now I will first tell you how did I meet each criteria. After that I will give you some tips to avoid some mistakes I made in my first application.

Meeting Optional Criteria 1 (OC1)

I am not a founder or a senior executive but the criteria also says I can show innovation “as an employee working on a new digital field or concept”. From 2017-2020 I have worked for a Indonesian startup called Traveloka in Singapore. I was one of the founding engineer of their fintech team and built couple of major products. So I requested one of the senior executive from the company to write a recommendation letter for me.

To support the recommendation letter, I have also submitted a letter from the HR of the company stating my employment period and role in the company. I also included a screenshot from my Github profile showing heat map of the code contribution for Traveloka but I am not sure if that played a significant role.

Meeting Optional Criteria 2 (OC2)

To fulfill OC2, I mainly leveraged my experience as a judge for the ACM International Collegiate Programming Contest (in the Dhaka, India, and Singapore Regionals), known as the the Olympics of programming competitions, and my published book. Shahriar Manzoor, the Dhaka Regional Director and world finals judge, provided me with a recommendation letter, emphasizing my contributions to the programming community, my tech blog, and my book.

As supporting evidence, I have attached a picture of myself awarding a prize at the final ceremony in the Singapore regional event. Also I attached screenshot of the Goodreads and Rokomari page of my book. My book was given as prize in one of the programming contest in my university, I have also used that as evidence.

Meeting Mandatory Criteria (MC)

For this, I have submitted a recommendation letter from my manager in my current job in Meta. He wrote some overview of my contributions and what sets me apart from other engineers. Also I have submitted a salary comparison for all my jobs. I have shown that I was consistently able to draw salary than average software engineer salary of the country I worked in. Of course I had to submit payslips from my jobs to support this. For this criteria, the evidences can’t be more than 5 years old.

Statement of Purpose

I had to write a SOP to make them understand how I will be beneficial for UK economy. I wrote a standard SOP talking about my startup experience and community contributions and expressed my desire to become a tech lead in a startup. Most of the startups in UK can’t sponsor visa, and a tier-1 visa can allow me to work for those companies. Looks like the reasoning was good enough.

After Getting The Endorsement

After I got the endorsement, I had to apply for the visa to UK government. They rarely reject applications if you have the endorsement unless you fail their background check. It takes 2-4 weeks on average to get the endorsement and about a month after that to get the actual visa.

The application process is not cheap, specially the health surcharge after getting the endorsement. Luckily Meta sponsored it for me but if you don’t have a sponsor, this is something to keep in mind.

Tips and Tricks

My first application was rejected (for MC and OC1) as my evidences wasn’t well organized and they mentioned some of my contributions are standard contribution for a senior software engineer. In the rejection letter they don’t give much detailed reasoning but if you appeal, they might give more details. In the next attempt, I requested the recommenders to explicitly mention why I was hired and what makes me exceptional and that time I had no issues.

If you select OC2 as optional criteria, your community contribution shouldn’t be a one time event, you need to show sustained effort over a period of time. You can start building profile by writing tech blogs, doing open source work etc. Don’t do anything just for the sake of the visa, Tech Nation don’t like that. For the recommendation letters, try to get from the senior most person possible. You also need to attach their CV or Linkedin profile to show their credentials.

The documents you submit might be slightly different if you apply for Exceptional Promise instead of Exceptional Talent. You can apply for promise if you are early in your career. The only difference between promise and talent is the time it will take to get a ILR in UK (5 years vs 3 years). One of my friend got the exceptional promise few years back, you can read his experience here.


That’s it, thank you for reading. If you have further questions, you can contact me in linkedin but do check the official website first. (Please don’t request for the copy of the letters unless you know me personally.)


Graph Algorithm 101 for Busy Engineers (Part 1)

This post is for the experienced software engineers who want to refresh their memory on graph algorithms quickly. I will not go into the details of any algorithms, rather than I will list down the most basic algorithms and will briefly tell you how they work. If you are working in the industry for a few years, chances are you have already forgotten most of the algorithms. However, I hope, this post will help you to bring back some of the memories. And certainly, you can google yourself later and learn the details.

(If you are a Bengali reader, you can check out the graph algorithm book which i wrote in 2016. Also, I have lots of articles about graph algorithm in my Bengali blog)

I assume the readers of this post are competent engineers, and they roughly know what a graph is. So, I will start with graph representation and will describe some algorithms very briefly. When I mention their complexity, I will use the variables $V$ and $E$, where $V$ is the number of nodes in the graph and $E$ is the number of edges in the graph.

Graph Representation

There are two significant ways to represent a graph; the first one is an Adjacency matrix, and the second one is an Adjacency list. Adjacency matrix is a $V \times V$ matrix where $matrix[u][v]$ represents if there is an edge between $u$ and $v$.

adjacency matrix

For a weighted graph, the matrix contains the weight of the edges. It takes $O(V^2)$ memory. While the matrix makes it easy to check the cost or existence of the edge between $u$ and $v$, the operation of finding all the edges connected to a node becomes expensive as you need to traverse the whole row. On the contrary, the Adjacency list can solve this problem. For the above graph, the Adjacency list will look like this:

Adjacency list

In C++, you can use a vector or, in Java, you can use an ArrayList to make this kind of list. It takes only $O(E)$ memory. But the downside is, you need to traverse the list to check if there is an edge between $u$ to $v$.

Now, the question is which representation is to use? Well, it depends on the type of problem you are solving and the type of operations you need to perform.

Breadth-first-search (BFS)

What does it do?

BFS is the most straightforward algorithm to find the shortest path in an unweighted graph. The problem we are trying to solve here is, “Given an undirected graph and a source node, find the shortest path from the source to every other node of the graph.”. It is a “Single source shortest path” (SSSP) algorithms.

How does it work?

Have a look at the graph below. Assume $1$ is the source node. bfs graph

BFS will traverse the graph level-by-level. Here, the level 1 nodes have distance $1$ from the source, then, the level $2$ has distance $2$ and so on. It uses a queue to achieve this. At first, put the source into the queue. Now while the queue is not empty, pop out the first element of the queue and push every connected node in the queue (don’t push the same node twice!!). Doing this, it will ensure you that you will push lower level nodes in the queue before higher level nodes. As a result, every time you push a node, you can calculate the level by adding $1$ to the level of the parent node.

And, the complexity of this algorithm is $O(V + E)$.


What does it do?

Dijkstra is quite similar to BFS. It also finds the shortest path, but in a weighted graph. Like BFS, it is also an SSSP algorithm.

How does it work?

As I said, it’s similar to BFS. But instead of a queue, it will use a priority queue. This priority queue will contain node numbers and distance of the nodes from the source. The members of the queue will be sorted by the distance.

For example, let’s assume $d[u]$ is the distance from the source to $u$. Every time you go from node $u$ to $v$, not only you push v to the queue, but also you need to push $d[v]$ to the queue. That means every time you pop a node from the queue, you will get the closest node from the source.

For this algorithm, the complexity is $O(V \times logV + E)$


What does it do?

Floyd Warshall is an all pair shortest path (APSP) algorithm. It can find the shortest path from all node to all other nodes in a directed path.

How does it work?

Even though the Floyd-Warshall algorithm is internally pretty complex, however, the code of this algorithm is surprisingly simple. First of all, you need to represent your graph using adjacency matrix, other representations won’t work. If two nodes don’t have a connection, you need to put infinite in those cells. Now you need to run $3$ nested loops. The first loop will select a node $k$ as the intermediate node. Then, the other two nested loop will select two nodes $u$ and $v$. Now you will check if it’s a good idea to visit $k$ while you are going from u to v. More specifically, you need to check if $distance[u][v] > distance[u][k] + distance[k][v]$. If the condition is true, you need to update $distance[u][v]$ and move on. In the end, the matrix will contain the shortest path between every node.

Here, the complexity of Floyd Warshall is $O(V^3)$.


What does it do?

Bellman form is another SSSP algorithm. But the speciality of this algorithm is, apart from finding the shortest path from the source, it can also detect the existence of a negative cycle in a graph. (A negative cycle is a sequence of edges which start and ends at the same node and the sum of the weight of the edges of the sequence is negative.)

How does it work?

Let’s assume $d[u]$ is the distance from the source to $u$. Initially, $d[source]$ is $0$, and for all other nodes, $d[u]$ is infinite. Then, imagine you are going from $u$ to $v$ and weight is cost(u, v). Now you can update the value of $d[v]$ if $d[u] + cost[u][v] < d[v]$. That means you found a path which is shorter. This is called “edge relaxation”. Now perform this relaxation for every node and keep updating the values in $d$. Then, you will get the shortest path from source to all nodes which uses at most 1 edge. Repeat these operations $V-1$ times and after that, you will have the correct shortest path. Remember, the shortest path can have at most $V-1$ edges.

Now, the concern is, how to detect a negative cycle? For this, first of all, you need to relax all the nodes one more time. Then, if the negative cycle is absent (no negative cycle at all), the value of any index of $d$ won’t update, because, there is no shortest path that has $V$ edges. And, if there is a negative cycle, at least one index will update. In the case, where a negative cycle is reachable from the source, the shortest path is not defined anymore as you can make the path as short as you want by going around the cycle many times.

The complexity of the algorithm is $O(V*E)$. If there is no specific reason, such as, the possibility of a negative cycle, don’t use this algorithm.

Topological Sorting

What does it do?

Imagine you have a list of tasks, each task is dependent on some other tasks. You can represent it with a graph like below:

topsortNow you need to find a correct order to complete the tasks. There can be more than one possible solutions. Topological sort or topsort can help you to do that.

How does it work?

Let us assume that there is no cycle in the graph, otherwise, there will be no valid order. Because a cycle will create a circular dependency, hence no valid ordering will be possible. At first, you need to find all the tasks which don’t depend on any other tasks. To do this, we count the in-degree of each node (the number of nodes has an edge towards the current node). The tasks with indegree $0$ can be performed first. Next, push those tasks in the list of answers and remove the outward nodes from those nodes. Now we will get some new tasks with in-degree zero, and we can perform them. Keep repeating this until you complete all the tasks.

Complexity of this algorithm is $O(V^2)$. In order to find all the possible orders of the tasks, you need to do backtracking.

That’s all for part 1, and I will talk about minimum spanning three, depth-first-search and some other algorithms in the next part.

Tech Retro: Pull Request Ethics

Pull requests are part and parcel of a soft engineer’s life unless you work in a horrible place where people just merge the codes to master directly. How a team handles the pull request and code reviews tells a lot about the team’s culture.

Last week we were discussing pull request ethics in our bi-weekly tech retros in Some of the points we discussed are, they should have meaning commit message and description and criticism about code should never offend anyone. If your team is relatively new, you can think about setting some ethical standard about pull requests to avoid conflicts and maintain healthy relationships. Here are my two cents about pull request ethics:

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