It is always difficult for recruiters to find ideal candidates, particularly when the sheer volume of candidates in the job market makes it hard to philter the quality candidates from the rest. The time, effort, and resources that go into hiring great candidates have been made more difficult by remote work to justify.

The most accurate, accessible, and underutilized source of candidates for recruiters is employee referrals. HR teams can leverage their relationships with current staff and get them to suggest valuable candidates. Within a fraction of the time and at a fraction of the cost of other candidate-sourcing channels, your staff can help you reach the most relevant candidates.

Referrals reduce hiring time

Employee referrals provide immediate access to talent because they are sourced through your employees’ personal connections. This cuts off a lot of time from the hiring process that would otherwise be spent on the cycles of sourcing and screening, coordination, negotiation, etc.

References enhance hire quality

Much of the world’s workforce consists of passive candidates. Despite the recent shift in the employment market, which has increased the number of job seekers, the highest talents in most industries often work in large and secure jobs. This means that they may not look for opportunities actively, but may be open to new roles. Such candidates can be reached with references. As you have already become fully aware of your vision, mission, culture, and atmosphere, your employees can tell you why people should think about your business.

Referrals boost retention rates

Referrals give your new hires the opportunity to understand your business from the point of view of an employee before they even begin to work for you. That implies that referrals understand what they are signing up for. They have company friends who can assist them to settle in quickly, reduce attrition, and boost retention. It is also true that workers who are prepared to refer to their relationships have faith in the business and are likely to stay longer. Your referral program can also be an employee satisfaction check in a sense.

Referrals get culture fits

Referrals also make it simple for people who fit in with the culture to be attracted. Referred applicants already know someone in the company and are familiar with what the culture is like, which implies that those references are more likely to be individuals who are first and foremost attracted to the culture.

Referrals help to attract potential clients

When your employees build brand awareness among their connections, they are not only tapping potential candidates, they are also creating prospective customers. The connections of your employees may vouch for your organization in the businesses for which they work, leading to positive word of mouth in larger circles for your brand.


Make sure you have your sourcing basics right before you hire through referrals. Watch out for common pitfalls that through referrals can come with sourcing candidates, and do not rely on referrals as the only source of talent. You should stick to the standard hiring process and provide timely updates to candidates if you don’t want diversity to take a hit, so you don’t leave them hanging, even if it means gracefully rejecting candidates.https://www.youtube.com/embed/dUoYXeUt8K4?feature=oembed&autoplay=1&playsinline=1&start=2&end&wmode=opaque&loop=0&controls=1&mute=0&rel=0&modestbranding=0


We are in an age where Data is everything. Amount of data burst every single second is humongous. Data science along with its different aspects focus on getting valuable information out of the data, for improvement of every system. 

Everybody knows data science is a hot cake that sells fast and offers the most lucrative professional opportunities in technology. 

Data analysis was first used to analyze data and it has evolved in modern data science. 

So, what is Data Science? To simply put, Data science is the method of deriving valuable information out of large sets of data. 

Data engineering focuses on building the architecture and platform for the collection of data and gives the data output in a usable form. 

The three main careers with data science are Data analysts, Data engineers and Data scientists. 

At first Data scientists had the role of both data scientists and data engineers, which is a huge skill set to cover. With the advent of big data, more streamlining required, thus came a new job role, data engineers or big data engineers. 

The number of skilful people in data science is low, and hence there is a huge career opportunity with data science. 

Let’s see the difference between data engineer, data analyst and data scientist. 

Data Engineer

Who is a data engineer? 

 A data engineer is a person who uses computer science engineering to build data infrastructure and pipelines for gathering and maintaining data. The main work of data engineers is to design, build, integrate and manage data and give data in a usable form for the data analysts and data scientists to compute with. They closely monitor the quality of data pipelines and infrastructure and ensure that data is accurate and easily accessible. 

Some companies term data engineering as data infrastructure or data architecture & the associated job role is data architect.

Data engineers don’t involve in the analysis of data; hence they don’t involve in decision making based on data. 

In short, data engineers receive, store, clean and prepare the data for data analysts and data scientists.

Education & Skill Set

A data engineer should mainly have a computer science engineering or software engineering background. They should possess strong programming skills such as Java, C#, Scala or Python. Sound knowledge in SQL and NoSQL databases (MongoDB, Cassandra DB) and frameworks like Apache Hadoop, Hive. Along with programming and database skills, data engineers should have strong mathematical(statistics) and big data skills. 

Salary Insights 

According to Glassdoor, the number of job openings for data engineers is almost five times higher than that of data scientists. Even in this pandemic, there is a drop-in data scientist job vacancy, but the openings of the data engineers are on the rise. 

As per PayScale, data engineers get around $63k to $131k yearly salary. The average salary of data engineers according to Glassdoor is $172k. DataCamp mentions salary of data engineers are minimum of $43K to $364k, based on years of experience and expertise. 

Data Analyst

Who is a data analyst? 

Data analysts cleanse and analyze data with the help of statistical tools to derive business decisions from historical data. They focus on finding insights for specific business goals. Data analyst job will typically analyze data for a specific query and present them in visual form. Data analyst to some extent helps the organization to make data-driven decisions based on historical data. 

Education & Skill Set 

A data analyst can be considered an entry-level job role. They don’t have to possess strong programming skills or algorithm skills. Graduates with mathematics, statistics or any math-related fields with a bachelor’s degree can pursue data analyst jobs. The essential skills of a data analyst include statistics, communication, business knowledge and fair knowledge in excel or any computing software, tableau and SQL. 

Salary Insights

According to Robert Half Technology’s 2020 Salary guide, Data analysts earn an average of $83k per year. 

Data Scientist

Who is a data scientist? 

Data scientists are actually predicting the future by manipulating data to solve critical business problems and provide predictive algorithms based on the model they used. 

The job role of data scientist and data analyst may look the same on a wider level, but data scientists work with a large volume of big data and use predictive analysis, machine learning and programming skills to automate the process.

A data scientist is responsible for cleaning, processing and verifying the data from data engineers and performing analysis on the data using advanced automation and present the business with useful insights and solutions. 

Education & Skill Set

Data scientists should typically possess a master’s degree in the field of Data Science. They should have extensive programming skills to create various algorithms for data automation. They should have command over mathematical and statistical skills. A data scientist should have hands-on knowledge in machine learning, coding, software development, data mining and data analysis, python, SQL and NoSQL database, python, Scala, Java, etc. 

Salary Insights 

According to PayScale and Glassdoor, the average salary of data scientists ranges from $80k – $130k. As per DataCamp, a data scientist earns $34k – $341k based on their experience and expertise. 

So, which one is for you? Data Analyst or Data Scientist or Data Engineer?

The job role mainly depends on your educational background and skill interest. If you are more inclined towards programming and love building things, data engineering is the way to go. 

You don’t like programming but would love to compute and work with data in an analytical & statistical manner – data analyst should be your job role. 

On the other hand, if you love the manipulation of data, along with programming skills and highly analytical – data scientist will be your ideal job. 


In the end, it is personal interests that matter. You can develop the right skills for any of the above job roles in data science if you invest time, money and determination to learn them! 

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