Big data has started to affect businesses and the global economy in a big way. BLS is predicting that big data-related jobs are going to increase by 12% by 2028. Companies are going to need highly skilled professionals to take over these jobs. As the experts for big data-related jobs are hard to find, the companies will offer attractive packages to the best candidates.

Most industries have begun to collect, organize, store, and interpret massive amounts of data related to their operations. According to a recent New Vantage Partners survey, 91.6 percent of executives polled said they were increasing their investments in Big Data. 91.7 percent of these respondents believed their investments were required to transform their operations into more agile and competitive businesses. Big Data is generating a lot of buzz in the IT job market throughout the world. In this article, we are going to list down seven big data jobs and the responsibilities entrusted with them. Let’s give in to the details.

Chief Big Data Officer

• In charge of the company data management budget as well as system initiatives related to data.
• Monitoring of data quality activities across the organization and serving as the primary authority for resolving data management concerns.
• Enforcing EIM principles as determined by the organization.
• Establishing the data policies and standards.

Big Data Engineer

• Collecting and processing of raw data, at a large scale.
• Analysis of data and processing the unstructured data.
• Ad hoc analysis and their usage to support business choices as needed.
• Collaborating directly with the technical team.

Big Data Architect

• Using best practices and technologies like such as SQL, SSIS, SSRS, etc. to design databases, data models, data warehouse applications, and many more.
• Deliver high-quality outputs, such as data warehouse architectures, and data system upgrades that adhere to current standards and norms.
• Analysis of the business needs and collaboration with project personnel to make choices and suggestions on future developments.

Database Manager

• Improving the present database architecture’s scalability and performance.
• Creating database architecture and functionality based on organizational requirements.
• Data protection via the development of data security and restoration policies, processes, and controls.
• Preparing and the delivery of system performance report to the top management.
• Process Documentation and adhere to database management best practices.

Database Admin

• Installation and upkeep of software
• Managing the data security and privacy
• Improvement of the performance of query processing
• Backups of Data
• Management of the integrity of data

Big Data Mining Administrator

• Using advanced statistical modeling to identify possibilities for increased productivity
• Examining user behavior to identify typical patterns or trends that may be utilized to improve the performance of a product
• Identify opportunities for improvement and communicate action plans to a supervisor or client.
• Through internal conversation, get knowledge of company needs and, when applicable, conduct relevant analyses.
• Understanding the company processes, goals, and strategy to assist management with analysis and interpretation.

Business Data Analyst

• Understanding the business strategy by collaborating closely with key business stakeholders, analysts, and senior management.
• Aligning the stakeholders and analysts on a focused perspective of how research and analytics may be used to support business goals and strategy.
• Partners with corporate stakeholders to frame and prioritize business issues, taking into account both near and long-term potential effects.
• Lead and engage in business discussions, offering an educated viewpoint and pushing for change where necessary.
• From start to finish, manages complicated analytics initiatives that necessitate multi-team collaboration among analysts or stakeholders.
• Manage numerous ongoing projects effectively, ensuring that objectives and timeframes are fulfilled. Determine short-term and long-term trade-offs, and balance the demands of stakeholders.

Database Developer

• Analyzing the existing databases of an organization is their major task. Also, they have to make sure it runs smoothly, make the necessary modifications, and get rid of unnecessary coding embedded in it.
• They need to be good team players as often, we will have to work with the rest of the development team.
• Candidates who are experienced in, the development of databases, data analysis, and testing are considered for this job.

Data Analysts

• Analyzing the data available and providing the higher management with the insights is their primary task.
• They have to develop their systems so that they could get accurate insights.

Data Scientist

• According to PayScale, experienced information technology (IT) data scientists capable of mining and understanding complicated data for large organizations have plenty of chances.
• They aggregate and generate various models of statistical data in collaboration with cross-functional IT teams to inform the formulation of system-related suggestions and action plans.

Security Engineer

• IT catastrophe planning, aversion, and mitigation requires the expertise of security engineers.
• By installing computer firewalls, detecting and responding to attacks, and forensically pinpointing system security vulnerabilities, they reduce company risk exposure.
• They also develop and implement test strategies for new or updated software and hardware, as well as multi-layered computer network defense methods.

Tips for Success with Big Data jobs.

1. Begin with small

Big data projects, in most organizations, get their start when an employer gets convinced that the company is not receiving opportunities in data.

Big data analytics can be performed with the software tools primarily used as part of robust analytics disciplines like data mining and predictive analytics. You are likely to find many unknowns when working with data that your organization has not used before, for instance, the bulk of unstructured information from the web. Which parts of the data carry value? What is the important metrics the data can provide? What are quality issues? Due to these unknowns, the time and costs required to get success can be difficult to predict.

So it’s better to start small. Start by defining a simple analytics that will not take time or data to run.

2. Understand your company’s requirements

Is your company ready for Big Data tools and solutions or not? If it takes a day or even more to achieve data inputs and analysis on essential business activity, then the company is not. This slow process can hamper the effectiveness of business decisions and badly affect revenues and returns.

Companies face a data dilemma when disruptors try to change the game or when adjacent industries are already making most of Big Data. The increased velocity of competition makes companies accept Big Data. The precision analytics in Big Data helps ‘nowcast’ instead of ‘forecast’ situations.

3. Budget for flexibility

Many companies over-estimate the number of reports they want as part of their new analytics, and this can be costly on the grounds of third-party development fees. It is highly cost-effective to assign the budget to craft a ‘self-service’ solution which allows users to make their reports as the need crops up.

4. The executive dashboard should be your priority

A user-friendly interface that delivers the right information to senior managers as fast as possible is the key to ensure that the system is used extensively. Data interpretation and data visualization experts can help develop a neat and efficient dashboard.

5. Use a solution-oriented approach

Though many advancements have been made in the Big Data ecosystem over the years, it is still budding as a platform that can be employed in production business deployments. A dire need for enterprise technology initiatives is likely to evolve and be a ‘work in progress.’

Software evaluators will not get one off-the-shelf tool that covers all present and forward-looking Big Data analytics requirements. Without over focusing on the term ‘future proofing’, extensibility and scalability should be a vital part of all project checklists.

The capability to port transformations to run consistently across different Big Data distributions is an advantage. But complete durability needs an overall platform approach to scalability, which is line with the open innovation that is driving the Big Data ecosystem.

Where Can You Find Jobs for Big Data?

The demand for labor has risen considerably in recent years as a result of the rapid proliferation of big data applications. According to LinkedIn's 2018 Workforce Report, there is a significant shortage of data scientists nationwide and in major cities: "Nationally, we have a shortage of 151,717 people with data science skills, with particularly acute shortages in New York City (34,032 people), the San Francisco Bay Area (31,798 people), and Los Angeles (12,251 people). Let's take a look at the top 8 jobs for big data.

Conclusion

This is a fast-growing area and would flourish in the upcoming years. Thus, it will generate thousands of data-related jobs for you. It is strongly recommended to collect the knowledge and skills required as most of these jobs will pay you exceeding 100,000 USD per month.

Big data jobs are going to be available in plenty after a few years from now. Most of the industries are rapidly growing and are relying on data than ever before making space for more and more data jobs. It won’t be a bad idea to make yourself equipped with the necessary knowledge and qualifications and be eligible for the data jobs as the trends suggest that they are going to take over the job marketplace soon.

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