Business Intelligence Developer I Income – Median Business Intelligence Developer Salary $92,540 To create our salary estimates, we start with data published in publicly available sources such as the US Bureau of Labor Statistics (BLS), Foreign Labor Certification Data Center (FLC) ) Show more
The average salary of a business intelligence developer in the United States is $92,540. Business intelligence developer salaries typically range between $70,000 and $121,000 per year. The average hourly rate for business intelligence developers is $44.49 per hour. Business intelligence developer salary is influenced by location, education and experience. Business intelligence developers earn the highest average salary in Washington, California, Oregon, New York and Nevada.
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The average salary of business intelligence developers in Washington, California and Oregon is the highest in the US The lowest average salary of business intelligence developers is Arkansas, North Dakota and Wyoming.
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Business intelligence developer salaries at Meta and Apple are the highest paid according to our latest salary estimates. In addition, the average accountant salary at companies like Google and Ropes & Gray are very competitive.
The average salary of business intelligence developers varies by industry: The average salary of business intelligence developers in the healthcare industry is $92,515, the highest of any industry. The average salary of business intelligence developers in the financial industry is $92,137. Business intelligence developers in the real estate industry earn an average salary of $88,531, the lowest of any industry.
Washington, D.C. is the highest paying Business Intelligence Developer in the US, with an average salary of $110,901 per year or $53.32 per hour.
You know if you’re being paid fairly as a business intelligence developer if your salary is close to the median salary for the state in which you live. For example, if you live in California you should be paid close to $109,094 per year. Course Report strives to create the most trusted content for coding bootcamps. Read more about Course Report’s editorial policy and how we make money.
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Data drives business decisions in every industry, which means companies need skilled business intelligence analysts, data analysts and data scientists. But what exactly is the difference between these three data career paths? Aaron Gallant, data expert and curriculum leader from TripleTen, explains the differences and similarities between data analytics vs. business intelligence vs. data science, and the responsibilities and typical salaries associated with these data roles. Find out who is hiring data professionals now and how TripleTen is helping students find data jobs with their data bootcamps.
Business intelligence is similar to other data specializations in terms of techniques, but focuses on reporting, data visualization and storytelling, and dashboards—the kind of things that influence business decisions and inform strategy. When a company talks about being “data-driven,” they’re probably talking about relying on business intelligence professionals to help inform their decisions.
What is data analysis? Data analysis is the overall process of understanding data and extracting information from it.
Data analysis also supports decision making and is based on a range of techniques such as exploratory data analysis, hypothesis testing and predictive analysis. It is similar to business intelligence in that data analysts often use data visualization to inform decisions, but unlike business intelligence, data analytics goes deeper into technical skills by using Python, performing forecasting, and automating some of the data analysis. .
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What is data science? Data science lies at the intersection of statistics and computing to build predictive systems.
Both have been around for a long time, but computers continue to advance, enabling new techniques in terms of statistics. Data science is still about understanding your data first, so it shares some basic technical skills with data analysis like loading, exploring, and cleaning data. However, instead of focusing on either interacting with people or building things to directly aid human decisions, data scientists collaborate with software engineers to build scalable predictive systems.
Business intelligence, data analytics and data science are all built on statistics. They all require a similar basic understanding of data, distributions, and data exploration. Plus, they all use some kind of computer tools. BI doesn’t use Python as much as data analytics or data science would, but you’re still using a computer, writing scripts, and doing things to make sense of the data.
Anyone working in the data field should have a basic understanding of data wrangling, sorting, and cleaning. Because BI and data analysts work more often with business, marketing, or sales teams, they rely on visualization and forecasting tools. Data scientists are more focused on the technical side of data, so the tools they use rely more on programming.
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Working with data is not all about your technical prowess! Because data affects many components of an organization, a good understanding of soft skills is important to succeed in your data career:
In the workplace as a BI analyst, your primary responsibility is to understand the needs of the business and communicate the results to your team. You might be interviewing stakeholders or applying business frameworks, such as marketing funnels and cohort analysis, which are models for quantitatively understanding business behavior. Your role may include cleaning data and then building reports and dashboards to communicate findings to decision makers in your organization.
On the job as a data analyst, your primary responsibility is to analyze data to draw relevant conclusions and predictions for an organization. Maybe you clean data and then create reports to share with your organization. As a data analyst, you will use Python and apply statistics to your data to forecast and predict future events. You can also design experiments and perform hypothesis testing.
In the workplace as a data scientist, your primary responsibility is to build and train sophisticated, predictive machine learning models on data in order to create intelligent systems. Maybe you prototype something that can be done with the data, such as product recommendations, and then work with the engineering team to build those prototypes.
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One of the great things about data-driven careers is that everyone has data, so potentially every industry has relevant openings! The most common data-intensive industries for which TripleTen graduates are hired are finance, insurance, medicine, government, commerce and technology. I have also seen graduates getting jobs in logistics and agriculture.
Can you go from BI analyst to data analyst to data scientist or vice versa?
There isn’t just one data career ladder. There is no real standardization of these data occupations, so job titles and descriptions can get confusing. What you need to keep in mind is that these professions have transferable skills and individual careers are very personal. You just need to have clear career goals and work towards them.
For example: you might start out as a BI analyst, but instead of working towards a career in data science (which means getting into statistics and coding), you might go into product management or people management.
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Traditionally, a data scientist job is reserved for someone with more experience, but that’s not the case with all employers these days. The thing about the tech industry is that your job title and responsibilities won’t always line up perfectly, so you might end up being hired as a data analyst and find that you’re actually doing things that are more like BI or data science.
TripleTen offers a Data Analytics Bootcamp, a BI Analytics Bootcamp, and a Data Science Bootcamp. Aaron, what is your advice for an applicant who is interested in data and trying to determine which of these bootcamps is the best fit for their career goals?
Overall, data scientists and data analysts will use Python and some engineering tools, while business intelligence careers work with people and businesses and do less engineering.
TripleTen’s data bootcamps vary in length, ranging from 5-10 months, which can also be a factor for someone to decide which bootcamp is right for them.
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We do not require a degree or any specific previous experience. All of TripleTen’s Bootcamps are designed to get you hired in the tech industry. The Data Science program is the longest among data-focused bootleggers because it has the most ground to cover! Because data is everywhere, students can use their past experience to excel in the job search.
If you are able to put in the time and focus, our programs are designed for you to succeed.
In data science we see other technical backgrounds, such as a Bachelor of Science degree, that didn’t pay off in the job market the way they wanted, so they looked for data science skills to elevate their career opportunities. For example: Someone with prior medical experience may learn about data and end up with a distinct understanding of data in the medical space.
Lately, prospective BI students often arrive with some exposure to the subject, such as an entry-level role in a spreadsheet-driven business, but not in a data-driven way, and they’re looking to strengthen those skills to guide their career in that direction.
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Definitely not. College can be a wonderful opportunity, but it’s not for everyone and that’s okay – today’s hiring managers know that! Traditional companies may require a bachelor’s degree and there are certain situations, such as teaching, when
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