My Story Of Remote Onboarding: Like Clockwork

-

 Just like Cloud in the software world is something that is remotely present, I will be sharing my remote onboarding experience today, thus the title- Cloud Onboarding.



The Hunt

The Covid outbreak led to a difficult time for the entire world and I was no different. There were challenges here and there. Yet, I was among those lucky folks who still have the honor of working for an organization. I am employed and contributing through WFH from the beginning of March.

Let’s start from the very beginning- I have had concrete career goals in mind but 2020 had other plans. Like everyone else, I was just watching the COVID situation unfurl. I was watching it affect all my goals and could feel the stress building up every single day. But all I could do was wait and watch!

And then, I had a stroke of good luck! One fine day, I decided to get off my ass and start looking out for opportunities that were more in line with my career goals. I always wanted to work with a fast-paced organization, no matter what the workload was. I wanted to work in a team where my work would have a direct impact on the company. I wanted to contribute towards the growth of an organization.

And I got this opportunity with LambdaTest, one of the fastest-growing cross-browser testing platforms.

I joined LambdaTest as a Senior Member of the Technical Staff in October 2020. At the time of writing this article, I have completed 3 weeks here and I would be sharing my journey with LambdaTest so far.

The Dawn

Right from the beginning, when I got my offer letter, I was in touch with the HR team. The team was kind enough to contact me multiple times to ensure everything was going fine. All of my requirements were taken care of smoothly.

I felt so comfortable when I joined the company. I didn’t feel any awkwardness while communicating with the team over Google Meet on my first day. The HR team introduced all the new joiners to the team and company. Also, we were introduced to the mentors and the respective teams we will be joining.

Initially, I was worried about the joining process. I wondered how I will manage this change because as a Developer, there is something more than just work. The team has to work together to achieve a common objective. And it really matters how you can work together with a team when you haven’t even met them personally. That feeling of personal touch adds on to the trust and initiates a connection with the members.

I was very much worried about it, but I would say hats off to the team at LambdaTest, especially the automation testing team. They made me feel so comfortable and within a week I started to feel that connection with the team. Every time I was stuck with something, my mates helped and pulled me out of the rat hole.

There was a pre-planned session for almost everything from finance to KTs. A JIRA board for onboarding was already in place and was shared with us in advance, adding a star to the smooth joining process. And while these were some generic sessions conducted for all the new joiners, I had some team-specific sessions too.

Three weeks in the team and I have already started to contribute to the product. In fact, my work was also a part of one of the latest releases. It feels great to work for a high energy company like LambdaTest and I am looking forward to having a fantastic journey in this Rocketship!



Do have a look at my channel on youtube: The Data Singh

Happy Learning

Comments

Post a Comment

Popular posts from this blog

Word Vectorization

-
Image
Introduction Machine Learning has become the hottest topic in Data Industry with increasing demand for professionals who can work in this domain. There is large amount of textual data present in internet and giant servers around the world. Just for some facts 1,209,600 new data producing social media users each day. 656 million tweets per day! More than 4 million hours of content uploaded to Youtube every day, with users watching 5.97 billion hours of Youtube videos each day. 67,305,600 Instagram posts uploaded each day There are over 2 billion monthly active Facebook users, compared to 1.44 billion at the start of 2015 and 1.65 at the start of 2016. Facebook has 1.32 billion daily active users on average as of June 2017 4.3 BILLION Facebook messages posted daily! 5.75 BILLION Facebook likes every day. 22 billion texts sent every day. 5.2 BILLION daily Google Searches in 2017. Need for Vectorization The amount of textual data is massive, and the problem with textual dat
Read more

Spidering the web with Python

-
Image
Introduction We will be talking about  Spidering/Scraping How to do it elegantly in python Limitations and restriction  In the previous posts, I shared some of the methods of text mining and analytics but one of the major and most important tasks before analytics is getting the data which we want to analyze. Text data is present all over in forms of blogs, articles, news, social feeds, posts etc and most of it is distributed to users in the form of API's, RSS feeds, Bulk downloads and Subscriptions. Some sites do not provide any means of pulling the data programmatically, this is where scrapping comes into the picture. Note: Scraping information from the sites which are not free or is not publically available can have serious consequences. Web Scraping is a technique of getting a web page in the form of HTML and parsing it to get the desired information. HTML is very complex in itself due to loose rules and a large number of attributes. Inform
Read more

Machine Learning -Solution or Problem

-
Image
The article will be divided into different sections as follows: Introduction to Machine Learning Types of Solutions Classification using Naive Bayes A brief about Machine Learning According to the definition by Wikipedia,  Machine learning  is the subfield of  computer science  that, according to  Arthur Samuel  in 1959, gives "computers the ability to learn without being explicitly programmed."  Machine Learning defines a set of problems that have to be evolved through the data by implying some algorithm. One factor that has to be kept in mind while defining a solution through ML is accuracy. Accuracy is very critical in case you are developing a solution in medical domain(cancer detection).There should be a threshold set for every solution which can be based on risk %age that is acceptable. A useful cheatsheet from Microsoft's site to sum up the use of different ML algorithms for the different type of problems. Types of solution Machine Lear
Read more