Big data is a buzzword that’s been doing the rounds in the corporate world of late. But it would be erroneous to think it’s just a passing trend.
With more than 2.5 quintillion bytes of data created worldwide, there is immense potential for growth in this field. Surely, someone has to collect, store and analyze such a vast quantity.
Almost every industry that runs digitally and uses electronic media requires big data specialists, whether IT, finance, administration, manufacturing, retail, or medicine.
As someone looking to make an entry into the world of big data, you can find several sub-fields, from security to data management to software development, which fit your skills and preferences.
And this job market is bound to expand further in the immediate future as technology develops and demand for skilled applicants increases.
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What is Data Science?
Almost every interaction conducted through technology involves some transfer of data, whether it’s purchasing items on Amazon, browsing Facebook, listening to music on Spotify, or even using the voice recognition feature in your smartphone.
Data science involves collecting, shaping, storing, managing, and analyzing this data. It is an essential resource for organizations as it allows them to make impactful data-driven decisions.
A pertinent example is Amazon’s data sets which remember your past purchase history, allowing it to customize your homepage. This proves just how useful data collection can be for the average shopper and Amazon in streamlining purchases.
Let’s suppose you searched for smartphones. In that case, Amazon will not spam you with baby items and grocery products; instead, they will suggest things related to your past purchase history, such as phone accessories.
Data science can also help keep track of habitual behavior and send reminders to customers to buy something based on a purchase pattern.
For example, if you order formula milk for your child every month, this technology helps companies offer smart deals around the time you’re expected to make a purchase. This allows buyers to catch a bargain and the brand to upsell products.
Data science can help boost revenue for brands and increase savings for customers by optimizing purchases.
But not just retail, data science is also improving services in other sectors as well, such as public health, by accelerating research and mitigating the spread of contagious viruses.
In the public health sphere, data science and analysis has immense scope in improving diagnostic accuracy and alerting people to critical health issues and outbreaks in time.
In the case of contagious diseases, such as the Coronavirus and Ebola, it can help track, trace, and improve quarantine measures.
Officials can harness this data to record who is infected and prevent it from further spreading. Plus, areas with high infections can be cordoned off with the use of data analytics.
In agriculture, data science techniques can be utilized for better food growth by predicting yield results, quality, and quantity. It also helps cut down food wastage within the supply chain.
Data science has numerous applications across a vast range of industries. Recently, nonprofit organizations have been using data science technology to predict funding needs and boost their fundraising.
It’s a smart move to pursue a career in data science, as it’s not just a field where there is a strong demand for applicants, but it also pays quite well. Data might be the catalyst for the rapidly digitalizing economy.
Experts in data science are needed in virtually every industry, not just in the IT realm, and one would be restricting themselves by applying to only tech companies.
However, if you’re looking to foray into this field, employers will demand a high level of education and competitive credentials.
Let’s take a look at some of the sub-fields potential candidates can break into with an advanced degree in data science.
1. Data Scientist
Data scientists must wade through large amounts of raw, processed, and complex data streams and extract insights and patterns to drive their organization’s business strategy.
They are the technical wizards responsible for organizing unstructured and structured data, interpreting it, and analyzing it to formulate business insights.
A data scientist’s job is much more technical and intensive than a data analyst’s, as it is focused on crunching large numbers and heavy statistics.
The ultimate goal of data scientists is to formulate a plan out of unprocessed data.
For this, they must be well-versed in technologies that are common in big data fields. Still, the scope of their job may require them to use customized technology for specific analysis.
As a general rule, data scientists must know how to:
- Perform queries against stored data
- Extract data and house it in a non-relational database
- Take the non-relational data and extract it to a flat file
- Wrangle data in R or Python
A vacancy in this position will require candidates to have strong knowledge of data analytics, including software tools, data visualization skills, and programming languages like R or Python.
While the job is quite challenging, the silver lining is the high pay, with an average salary of $91,494. Also, the demand for data scientists is very high in the current market.
Pros
- High demand for skilled experts
- high-paying career
Cons
- Comprehensive domain knowledge required
2. Machine Learning Engineer
Machine learning is in tough competition with artificial intelligence as one of the most sought-after positions in the field of big data. It’s poised to be one of the most demanded data science skills in the years to come.
Machine learning engineers are responsible for creating data funnels and coming up with software solutions. They are expected to have strong programming skills, a propensity to work with statistics, and a software engineering background.
They devise data analysis software that can run components automatically and remove the need for human supervision. As organizations are rapidly switching to automatic infrastructures and hardware, Machine Learning engineers are more in demand and are paid $114,826 per year on average.
An ML engineer must not only design and build machine learning systems, but they must also routinely inspect and monitor these systems, running tests to measure their performance and efficiency.
The job descriptions for a Machine Learning engineer would include:
- Devising machine learning solutions for systems and components
- Establishing and ensuring a steady data flow between the database and backend system
- Analyzing data on a device-by-device case
- Running ML tests combining a specific programming language with ML libraries
Pros
- Abundance of positions
- high-paying career
Cons
- Fast-changing field
3. Operations Analyst
Operations analysts work internally in large companies as essential cogs in the machinery. However, many professionals also choose to work as consultants.
Their job description entails analyzing a business’s internal processes, which can include internal reporting systems, distribution systems, and manufacturing processes.
They are also responsible for the streamlining of general business operations.
Operations analysts must have technical knowledge of all the processes involved in a business and be meticulously vetted in every system. They are essential in every industry, from a grocery store to a military hardware plant.
Due to the versatile nature of this job, the salary can vary from industry to industry, but on average, operations analysts make about $67,353 a year.
Pros
- High demand for applicants
- Stable career trajectory
Cons
- Long working hours
4. Marketing Analyst
The field of digital marketing demands a strong knowledge of data, including market trends, competitor strategies, and customer responses.
Data analysts have ample opportunity to apply their marketing skills, whether as a prescribed analytics role within an organization or as part of a more extensive skill set.
Data science and marketing analysis require Google Analytics and other customized reporting tools to track traffic on search engines and social media platforms.
These days, marketing professionals must be acquainted with data analytics, and this is where data scientists shine.
A marketing data analyst must have an eye for analyzing traffic and making business decisions, choosing which marketing campaign to focus on and which to discontinue.
For this, they can work closely with other marketing professionals regarding how best to leverage resources.
Marketing analysts can expect to make an average of $66,571, with that figure rising to six-figures for senior-level positions.
Pros
- Excellent for creative-minded individuals
- Job opportunities in many fields
Cons
- Long working hours
5. Project Manager
A project manager’s job is multi-dimensional and varied, with the responsibility of tracking their team’s progress, efficiency and enhancing their productivity by improvising current processes.
As a general job description, a project management job is entirely administrative. Still, they must use diagnostic tools, incorporate data science, map out their processes and operation, and keep track of the output.
For project managers to excel, they must have an ample understanding of data analytics.
Vacancies in this position are found internally within organizations and separately in management consultancies.
They often overlap with supply chain management, which many project managers tend to switch to as a career trajectory. Furthermore, the average annual salary for a project manager is around $73,247.
Pros
- High upward mobility
- Job opportunities in many fields
Cons
- Responsible for many operations and processes simultaneously
6. Data Architect
Data architect jobs are wildly popular these days, and qualified data architects are in demand by companies looking to devise a data-backed strategy.
The job description for a data architect is quite extensive. They must know their way around large-scale databases, be well-versed in data analysis and machine learning, and have sound knowledge of a range of programming languages: C#, HTML5, Spark, and Python, JavaScript, SQL, among others.
Employers seek advanced technological proficiency, ample knowledge of programming languages, creative visualization, and a detail-oriented approach. Candidates with a computer-science background are preferred.
Data architects are responsible for ensuring data solutions are up to standard and come up with analytics applications for different platforms.
Alongside this, they must find ways to improve and maintain the performance and efficiency of existing systems while working closely with database administrators.
A data architect’s average salary is on the higher end of the spectrum, with most professionals making an average of $108,278 per year.
Pros
- High-paying field
- Massive demand for skilled applicants
Cons
- Monotonous nature of work
7. Data Engineer
A data engineer is responsible for performing batch or real-time processing of gathered and stored data.
They are required to build data transportation systems, called data pipelines, creating and sustaining a data ecosystem. This ecosystem must be interconnected and accessible by data scientists.
Data engineers focus on large datasets, and their job requires them to optimize the infrastructure for various analytical processes.
This might entail capturing data to increase an acquisition pipeline’s efficiency or upgrading a database infrastructure to deal with queries faster.
Those applying for this position are required to be well-acquainted with SQL database design and Linux systems.
They will also be tested for their command over programming languages, including Java, Python, Kafka, and Hive.
Data engineers are among the higher-paid data science professionals, with an average salary of $102,864 per year.
Pros
- High-paying field
- Significant demand for skilled applicants
Cons
- Long working hours
8. Business Intelligence Developer
Business intelligence developers are responsible for designing and developing strategies to help users locate data resources to make better business decisions.
These professionals should be masters at using reporting tools for managing data warehouses within an organization.
Moreover, BI developers must know how to use customized BI and analytic tools to assist the users’ understanding of systems.
They are expected to work closely with BI analysts to sort and interpret data for formulating strategies for the organization and users.
Lastly, BI professionals must have a strong understanding of business functions and quality assurance methodologies. They can expect to earn an average of $81,514 a year.
Pros
- High-paying field
- Job opportunities in many fields
Cons
- Requires high technical knowledge
9. Statistician
Statisticians are expected to effectively collect, analyze, and interpret structured and unstructured data to identify trends and relationships.
Their findings can be used at the organizational level to make effective decisions.
Of all the data science jobs, a statistician’s job is probably the most intensive. Employees in this position are expected to wade through large quantities of data to inspect and analyze them.
They are also responsible for designing data collection processes, communicating their findings to decision-makers, and assisting in drafting strategies upon interpreting data.
Statisticians can expect to make an average of approximately $76,884 per year.
Pros
- Jobs in many fields
- Not responsible for making any decisions
Cons
- Monotonous nature of work
10. Data Analyst
A data analyst’s job is one of the most coveted positions in the big data scene. What’s more, the demand for data analyst professionals exceeds that of others in the data game.
As a data analyst, you’ll be expected to have strong analytical skills with a statistical background. Experts should be well-versed in Hadoop, Hive, and Apache, among other vital tools.
A background in algorithms is also desirable to source information, and you will need to know domains at the tip of your fingers.
Your job description will entail tracking web analytics, analyzing A/B testing, and tracking and manipulating large data sets for analysis, among many other detailed tasks.
Candidates applying in this field must be tech-savvy and have ample knowledge of SQL databases. Besides that, they must assist in decision-making processes at the operational level and have strong presentation skills to communicate trends and insights gained from their analysis.
Pros
- Jobs in many fields
- High upward mobility
Cons
- High competition for fresh applicants
Breaking Into The Data Science Field
Data science experts are in demand in just about every industry, from security to healthcare. Countless organizations depend on data management solutions for better service provision and steady revenues.
If anything, the demand for data scientists will only soar as more and more organizations switch towards big data integration and solutions.
If you’re looking for your big break in data science, you will have to fulfill specific requirements to qualify for a job in the field.
Initially, you will most certainly need to acquire an advanced degree in your area of interest. Not only that, but you must be able to demonstrate your expertise to your future employers.
Additionally, having past work experience in your field never hurts. Most employers prioritize candidates with practical experience in the area.
Applying For A Job In Big Data
The best part about seeking a job in the data science industry is that there are more jobs than applicants.
Finding employment should not be a problem, but make sure not to get intimidated by job titles, as most positions are interchangeable and fluid.
A position hiring for a data scientist might accommodate a data analyst or a data architect as well. Focus on the job description and the nature of the job rather than the title.
If you’ve already spent some time in the field and have adequate experience, finding a job in big data should not be a problem.
Cast a net within your organization to see if they are hiring for any positions in data science. Most large organizations feature a big data department, and moving laterally within your company will be a more comfortable experience.
When applying for a new position, ask if they offer any training or paid education. And if you have expertise in a specific area of big data, make sure to highlight that.
Don’t be afraid to step outside of your comfort zone, even if the position listed might be a stretch for you.
Many organizations take on fresh candidates, offer on-the-job training, and even pay to get them certified. If they feel you have the ambition for it, they might take you on even if you lack experience.
Skills That Work Well In Big Data
Regardless of which sub-field you’re applying to, a data science expert is expected to have a solid background in programming.
Typically, you will be expected to command C, Python, Java, and SQL. If you’re branching off into analytics and statistics, you will need to brush up on specialized skills other than coding.
Although not a hard-and-fast rule, but candidates might be required to have Hadoop, Apache Spark, and machine learning certifications.
Alongside that, all candidates must have a strong base in mathematics and be well-acquainted with statistics and linear algebra. If you have some knowledge in cryptography, that’s a bonus in this field.
What a Career in Big Data Offers You
Big data jobs are quite lucrative and rewarding. Depending on the specific position and the skill-set and qualification, candidates can expect an annual pay scale of around $50,000 to $165,000.
While the demands can be challenging, it exposes candidates to the latest technologies while being offered generous pay.
The algorithmic demands explain why engineers, managers, and developers occupy the top-tier jobs in the field of big data. The engineers are responsible for devising architecture that can house data while developers come up with programs that collect and organize data and monitor security.
Positions for data analysts, statisticians, and specialists are slightly lower in demand, although not any less rewarding.
As an aspirant for work in data, you have a lot of leverage in the sub-field and industry you opt to enter and numerous opportunities to apply yourself in different niches.
For example, with a job as a data science expert in law enforcement, you will be writing code that searches through criminal records or developing a way to store biometric data more efficiently.
Moreover, if you work in finance, you would be responsible for managing bank accounts and analyzing cash figures. The options are endless.
If you opt for a career in big data, you’re set for the long term. The higher your qualification and skill-set, the more chances you will climb the career ladder!
Also, with more skills, you have more leverage to work with different employers or even start your own company.
If you are interested in a long-term career in big data, consider investing in quality certifications that go up to the graduate level or even higher. These will allow you to have your pick of the best jobs available anywhere.
FAQs
Is Data Science Still In Demand In 2021?
According to IBM predictions, data scientists’ demand was expected to grow by a third by 2020. Another report states that data science jobs will expand to include machine learning skills, particularly those connected to IoT and cloud technologies.
Do Data Scientists Make Good Money?
Data scientists can expect to make over six figures annually once they are established in their careers. For beginners, the pay scale is a little closer to $70,000, still quite lucrative for an entry-level salary.
