1. What is the average salary of a Business Intelligence Specialist V?
The average annual salary of Business Intelligence Specialist V is $161,521.
In case you are finding an easy salary calculator,
the average hourly pay of Business Intelligence Specialist V is $78;
the average weekly pay of Business Intelligence Specialist V is $3,106;
the average monthly pay of Business Intelligence Specialist V is $13,460.
2. Where can a Business Intelligence Specialist V earn the most?
A Business Intelligence Specialist V's earning potential can vary widely depending on several factors, including location, industry, experience, education, and the specific employer.
According to the latest salary data by Salary.com, a Business Intelligence Specialist V earns the most in San Jose, CA, where the annual salary of a Business Intelligence Specialist V is $202,709.
3. What is the highest pay for Business Intelligence Specialist V?
The highest pay for Business Intelligence Specialist V is $199,575.
4. What is the lowest pay for Business Intelligence Specialist V?
The lowest pay for Business Intelligence Specialist V is $126,521.
5. What are the responsibilities of Business Intelligence Specialist V?
Business Intelligence Specialist V creates reports, visualizations, dashboards, and metrics that provide business insight and aid in business decision-making. Uses querying languages like SQL, scripting languages like R or Python, and other tools like Tableau or Excel to produce reports and perform meaningful quantitative or qualitative analyses addressing impactful business issues or questions. Being a Business Intelligence Specialist V combines these reports with subject-matter expertise to deliver insightful takeaways and advice. Collaborates with project stakeholders to better understand valuable objectives and KPIs and to design relevant reports and dashboards. Additionally, Business Intelligence Specialist V requires a bachelor's degree. Typically reports to a manager. The Business Intelligence Specialist V works autonomously. Goals are generally communicated in "solution" or project goal terms. May provide a leadership role for the work group through knowledge in the area of specialization. Works on advanced, complex technical projects or business issues requiring state of the art technical or industry knowledge. To be a Business Intelligence Specialist V typically requires 10+ years of related experience.
6. What are the skills of Business Intelligence Specialist V
Specify the abilities and skills that a person needs in order to carry out the specified job duties. Each competency has five to ten behavioral assertions that can be observed, each with a corresponding performance level (from one to five) that is required for a particular job.
1.)
Analysis: Analysis is the process of considering something carefully or using statistical methods in order to understand it or explain it.
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Computer Science: Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to practical disciplines.
3.)
Data Analytics: Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
