LinkedIn has nearly 6.5 million job listings. 97% of recruiters currently use LinkedIn to verify the experience, skills and overall assessment of a candidate. According to a recent study – 89% of all recruiters world over, have admitted to hiring somebody based on their LinkedIn profile. With all these proven facts – it is absolutely necessary to create the perfect LinkedIn profile, in order to secure the right job to start your career in Big Data analytics. A JobVite survey reveals that 78% of recruiters hire through a social network of which LinkedIn is the leading network for hiring. 92% of the jobs are posted on LinkedIn vs. 39% on Twitter and 48% on Facebook.
While 97% of recruiters use LinkedIn to find candidates, only 36% of the job seekers are active on LinkedIn. This striking difference leaves you, the big data professional, with a huge advantage to make the most out of LinkedIn, in finding lucrative big data jobs to fulfil your career goals.
Career Counsellors at DeZyre often get questions from students mentioning that – “I have a LinkedIn profile but I hardly get any calls or messages from recruiters regarding big data job opportunities.” We hope that this blog post will solve all your queries related to crafting a winning LinkedIn profile.
LinkedIn is a great career search and networking tool but just creating a LinkedIn profile like any other social networking profile (Facebook or Twitter) will not help you get the right exposure and more often than not, your profile will not be visible by head-hunters. You will need a complete 100% LinkedIn profile overhaul to land a top gig as a Hadoop Developer, Hadoop Administrator, Data Scientist or any other big data job role. DeZyre has got you covered in this blog post, with all you need to know about your LinkedIn profile right from creating a spectacular LinkedIn summary to selling your big data accomplishments, Hadoop projects and Hadoop skills all in one go. Setting up and optimizing your LinkedIn profile to get noticed by recruiters in the big data space takes time. It might just take you few minutes to read through this post but you will have to spend considerably greater amount of time refining your LinkedIn profile for Hadoop jobs based on the suggestions listed in this article.
People often confuse LinkedIn profile with an online resume but that’s not true. A resume can be one or two pages but you can craft your LinkedIn profile to contain more professional details on the various projects you have worked on, a complete professional history of your experiences, a professional photo, etc. that are usually not present in a resume. Also the interaction that happens on LinkedIn cannot be achieved on a resume. You can connect with people who have similar skills and backgrounds on LinkedIn and expand your professional network. For example, if you are an individual looking for a Hadoop job then you can join various big data and Hadoop groups related to the tools and technologies in the big data domain - to expand your network with connections who have similar interests.
Making a great first impression is like getting off to a good start in finding the best opportunities in big data and data science space. Your LinkedIn profile is the foremost opportunity that helps a potential employer find out who you are beyond the photo and the job title. An exemplary LinkedIn profile can mean the difference between getting big data job prospects and profile views to getting interview calls from your dream company finding you and offering you a Hadoop job or any other big data job. A LinkedIn profile is like your public mirror, it has to reflect all your accomplishments and experience.
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Researches reveal that a complete LinkedIn profile with 100% completeness is 40 times more likely to receive job opportunities through LinkedIn. An “all-star” LinkedIn profile is likely to rank higher in LinkedIn internal search algorithm which means that you are more likely to be discovered by big data recruiters. Here’s what it takes to create a complete LinkedIn profile -
WOO HOO!!! Your LinkedIn profile is now 100% complete but is that all you need to get noticed by the big data recruiters? The answer is a big NO. There is a lot more that needs to be done, in order to optimize your LinkedIn profile - to make the most out of all the available big data job opportunities and get noticed by recruiters.
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People often make the mistake of leaving the summary section blank but without a summary, your LinkedIn profile will be like a boat without a paddle. Your LinkedIn summary will help point the big data recruiters in the right direction, by giving them an idea on where to focus in your profile. If you are Hadoop developer looking for a lucrative job in the big data space, then use all the 2000 words in the summary to do essential keyword optimization. Headline, Summary, Job Titles are the most important spots for keyword optimization in your LinkedIn profile.
Keywords play a vital role on LinkedIn because the search algorithms that big data recruiters use to find the right talent, are based on the keywords from the open big data job descriptions. To ensure that big data recruiters find you for the right Hadoop job, focus on highlighting the specific Hadoop skills, spark skills or data science skills you want to work with, such as Pig & Hive , HBase, Oozie and Zookeeper, Apache Spark, Scala, machine learning, python, R language, etc. At the same time, you have to ensure that you remove all the technologies or skills that you are not interested to work with.
Say for instance you earlier worked as a Database Administrator but no longer want recruiters to find you for jobs related to administration then ensure that you limit using keywords related to database administration in your LinkedIn profile summary and skills. If you do not de-emphasize these skills, then recruiters might contact you for database administration jobs instead of Hadoop jobs.
By including keywords in your LinkedIn profile summary you can ensure that your profile performs well in search results for open big data jobs and the probability that you will be found by top big data recruiters is high. Your LinkedIn profile summary is the best place to inject relevant keywords based on your expertise. For example, if you are looking for Hadoop jobs then the probable keywords that you can find in Hadoop job descriptions are – MapReduce applications, Hadoop cluster, Hadoop cluster administrator, Hadoop distributions, Pig, Hive, etc. Ensure that you include all these keywords within your summary to get more traffic from interested big data recruiters. Weave your success stories using these keywords to show the recruiters how you have used these Hadoop skills.
A LinkedIn profile summary should be –
Here are a few sample summaries for Hadoop jobs that have been keyword optimized. These Hadoop profile summaries should give you an idea on how you have to draft the summary for your LinkedIn profile if you are looking for Hadoop jobs.
The world of analytics and data science is purely skills-based and there are ample skills and technologies like Hadoop, Spark, NoSQL, Python, R, Tableau, etc. that you need to learn to pursue a lucrative career in the industry. You can add all the relevant skills and technologies in the big data and data science space that you know and want to work on. If you are an expert looking for a Hadoop job opportunity then you can add all your Hadoop related skills like MapReduce, HBase, Pig, Hive, etc. into the skills section of your LinkedIn profile. If you want to become a data scientist then you can add all your data science related skills like Python, R, Machine Learning, Data Visualisation, Tableau, Statistical Analysis, Math, etc.
Recruiters who browse your LinkedIn profile can get a glimpse of all the Hadoop or data science related skills that you are proficient in, making it easier for them to find out if you are the best fit for the open job opportunity.
This is the skills recommendation for an existing Hadoop developer who has created an amazing profile in LinkedIn
Having the relevant known big data skills listed on your LinkedIn profile will let your connections endorse you for these skills. Your big data skills with most endorsements will appear at the top of list providing the recruiters an indication on your expertise points in the Hadoop ecosystem.
This is extremely important if you are a fresher. If you are a fresher looking for a Hadoop job opportunity, you need to work on several hands-on Hadoop projects to get a hands on experience on the technology or you might have made an attempt to solve real world big data problems. If you have these projects made public such as on a blog that you own or if you have taken a course with DeZyre you can showcase your Hadoop project portfolio by linking it to your DeZyre account or you can showcase your project work with the help of code repository site GitHub. Linking your profile to the Hadoop projects you have worked on will help potential big data employers understand that you have a working knowledge of the technology and they are more likely to hire you.
Adding certification to your LinkedIn profile means a lot more than getting recommendations. Certifications by a known MOOC provider like Coursera, Udacity and DeZyre will help big data recruiters trust you more and land you a top gig in the big data industry. It is better to add all the certifications that you have. Include all the certifications from top big data certification providers like Cloudera, Hortonworks, MapR, IBM or EMC and also from the MOOC providers that you have learnt these technologies from.Here's the link to a LinkedIn profile highlighting certififcations from top MOOC's -
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Recommendation can strengthen your LinkedIn profile to a great extent as they are third party validations of your skills and have greater chance of attracting employer’s attention. Aim for 5 to 10 recommendations, you can do so by getting at least 2 or 3 recommendations for each job. Big data recruiters weigh in these recommendations to a great extent so ask your connections to write them for you if they have worked with you. If you do not want to ask then the best way is to write recommendations for them and most of them are likely to reciprocate.
If you have got something brag-worthy then do not miss out highlighting it in Awards & Honours section of your LinkedIn profile
There are multiple LinkedIn groups for big data and data science so you should join a handful of them of better networking opportunities and to get noticed by recruiters. We have put together a list of top big data and data science groups that you must join on LinkedIn –
These are some of the top LinkedIn groups for Hadoop wizards and data scientists to discuss the latest technological innovations in the big data and data science space. You can find several experts active in these discussion groups and also many big data recruiters look into these LinkedIn groups to hire the next big data talent in their companies. As questions, take part in the ongoing discussions and share relevant articles which will not only make you an active member of the group but also boost your credibility.
LinkedIn’s search algorithm does not function like other search engine algorithms. LinkedIn’s search algorithm first finds people who are immediately connected to you and then it goes on to finding your second degree connections and after that finds people who know someone that you know and then the third-degree connections are discovered. This implies the more the number of relevant connections in your network with desired profile, more is the probability to get discovered by big data recruiters.
Connect with as many big data and data science experts to get noticed by them. Is it enough if you connect, absolutely not. Engaging your connections with targeted updates will help you get more visibility on LinkedIn.
LinkedIn’s job board allows you to access multiple jobs that big data recruiters pay LinkedIn to feature and big data Hadoop jobs posted on other websites that LinkedIn aggregates from the webs. You can customize which ads to show up on your LinkedIn job board based on location, industry, company size and seniority level as shown below -
That’s the simple job search which might not be so helpful to find the right job specific to your skills. A better way to find open big data positions is to click on the advanced search as shown below and filter jobs through relevant keywords like Hadoop MapReduce, Spark, Python, R, MongoDB, Pig, Hive, HBase. The advanced job search option will also help you search for open jobs with desired job titles likes’ Hadoop developer, Hadoop engineer, Hadoop admin, Hadoop architect, data scientists, etc. With the location specific search, you can look for Hadoop jobs in India, Hadoop jobs in United States or any other location based on your preferences. These are some of the advanced job search filters available for free LinkedIn accounts and to make use of additional Hadoop LinkedIn job filters like salary, company size, experience in years, etc. , you can upgrade to a premium LinkedIn account.
If you feel that the process of fine tuning your LinkedIn profile is overwhelming and tedious then the best way to do it is spend 20 minutes every day and emphasize on refining one section of your profile. Having created a winning LinkedIn profile summary, spend 15 minutes a day on LinkedIn learning, connecting and updating your profile and it will surely pay off. This way you will have a noteworthy LinkedIn profile in just few days that will help you get noticed by big data recruiters. LinkedIn is the big boss of the big data job market, so start injecting in some of your awesomeness into your LinkedIn profile, to fulfil your career goals.
Landing a big data job of your interest is easy if you have the desired skills and a great LinkedIn profile to show off those skills. Get that phone ringing NOW from top big data recruiters by using these guidelines to craft a perfect LinkedIn profile to land your dream job in the big data space.
Hope you have enjoyed reading this article and found out the benefits of how you can use LinkedIn to benefit your big data career. Feel free to share it with your peers using the social media icons on the left to help the big data community at large.