Business Intelligence Analyst Salary Seattle
Business Intelligence Analyst Salary Seattle – Data science is a field that encompasses many different disciplines. But all these data science roles require strong analytical skills, technical knowledge, and the ability to get actionable insights from a bunch of information.
As more and more industries use innovative data applications – from the healthcare industry using data science to improve patient care and drug development to Spotify using music data for personalized recommendations – data professionals from all walks of life types are in high demand. And there are a plethora of diverse job opportunities that cater to a wide range of passions and interests.
Business Intelligence Analyst Salary Seattle
Data science professions receive high salaries because there is not enough supply (the profession has nearly 20,000 open positions in the United States, according to US News) and their skills are in high demand.
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Data scientists are seeing this demand because, in recent years, an increasing number of organizations have begun to see the importance of making data-driven decisions. Whether e-commerce companies making inroads into artificial intelligence or travel companies incorporating machine learning into their platforms, data scientists are at the forefront of the latest technological revolution.
In the United States, data science professionals typically earn around $117,000 a year, according to Glassdoor. However, this can vary depending on a number of factors, including years of experience, level of education, industry, location, and area of expertise. For example, in large cities like New York or San Francisco, data scientists can earn up to $180,000. Machine learning engineers generally earn more than data analysts, and those who work in the consumer technology industry generally earn more than those who work for government agencies, nonprofits, or healthcare organizations.
Data scientist responsibilities can vary from company to company. Generally speaking, data scientists obtain and process the data that is available to a company.
The average annual salary for a data scientist in the United States is around $117,000, according to Glassdoor.
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Data engineers create pipelines that pipe data from different sources. They focus on raw data production readiness and elements such as formats, resiliency, scalability, data storage, and security.
The average annual salary for a data engineer in the United States is around $113,000, according to Glassdoor.
Data architects visualize and design an organization’s data management structure. They do this by evaluating an organization’s data sources and then designing a plan to integrate, centralize, and maintain the collected data.
– Application server software such as Oracle, user interface and query software such as IBM DB2 and database management software such as Microsoft SQL Server
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The average annual salary for a data architect in the United States is around $152,000, according to Glassdoor.
A data modeler translates real-world business needs into data models. They work closely with data architects to create conceptual, logical, and physical data models. They support application teams when designing databases and collaborate with data governance teams for compliance.
The average annual salary for a data modeler in the United States is around $106,000, according to Glassdoor.
Big data engineering focuses on the infrastructure that allows people to collect and organize massive amounts of data. This makes billions of clicks, taps, likes, swipes, shares and purchases usable for data analysis. They do this by building data pipelines, designing and managing data infrastructures, handling data storage and focusing on the ETL (Extract, Transform, Load) process.
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The average annual salary for a big data engineer in the United States is around $125,000, according to Glassdoor.
A machine learning (ML) engineer is a programmer who designs self-executing software that uses data and automates predictive models. ML engineers bridge the gap between data and software, creating programs that allow machines to run without direct human assistance.
The average annual salary for a machine learning engineer in the United States is around $123,000, according to Glassdoor.
Machine Learning is a subset of data science. While machine learning engineers and AI engineers do some of the same tasks, artificial intelligence (AI) engineering places greater emphasis on developing systems and machines that mimic human cognitive function, which makes them more adept at problem solving. .
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The average annual salary for an AI engineer in the United States is around $118,000, according to Glassdoor.
An enterprise architect is an IT professional who oversees an organization’s networks and services. They update systems across the enterprise, replace hardware or software, and determine which products and services are best for an organization’s support teams.
The average annual salary for an enterprise architect in the United States is around $164,000, according to Glassdoor.
Business intelligence (BI) analysts leverage their data for decision making. They collaborate with business teams to understand their needs, review data, and write financial and market intelligence reports.
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Natural language processing engineers build systems and devices that can understand human language. A subset of artificial intelligence and machine learning, NLP engineers operate at the intersection of computer science, information science, AI, and linguistics.
The average annual salary for an NLP engineer in the United States is around $88,000, according to Glassdoor.
Database managers are often confused with database administrators, but their roles are different. While the former creates, updates and deletes databases, a database manager maintains the results and performance of the database. Database managers also maintain the security of an organization’s databases.
The average annual salary for a database manager in the United States is around $76,000, according to Glassdoor.
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The average annual salary for a database developer in the United States is around $97,000, according to Glassdoor.
A stepping stone to a career in data science, data analysts identify trends, patterns and relationships in data to inform business decisions. Data analysts extract data, organize insightful findings, create visualizations and present reports to stakeholders.
– Data cleansing to rid data of errors and discrepancies and gain valuable insights by identifying patterns, relationships, and trends
– Familiarity with data analysis tools such as Tableau, Google Analytics, Jupyter, Amazon Web Services and Microsoft Excel
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The average annual salary for a data analyst in the United States is around $74,000, according to Glassdoor.
The more a company depends on data, the more it tends to pay its data scientists. Some of the highest-paying data science companies are social networking sites (which gather user data), e-commerce platforms (which track billions of data points about consumer behavior), and real-time services (which rely on data to be responsive to customers).
Amazon’s data scientists work across all of its verticals, from analyzing consumer purchase data on its e-commerce platform to analyzing user behavior for its advertising division.
The average base salary for an Amazon data scientist is around $137,000, according to Glassdoor, although Amazon employees generally earn much more. In 2022, Amazon raised its salary cap to $350,000 for corporate and tech employees, citing competition for tech talent.
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Uber’s data scientists work in product analytics (e.g. ensuring the accuracy of maps and geolocation technologies), finance and operations (e.g. using data to help the company with its financial planning and competitive strategy), and marketing ( for example, leveraging analytics insights to help improve growth, brand awareness, customer retention, and engagement).
Meta data scientists work on products like Facebook, Instagram, Oculus, Messenger and WhatsApp. An Instagram-focused data scientist can help your monetization division drive a long-term strategy for revenue sharing. A WhatsApp business analyst can collaborate with sales and marketing teams to identify opportunities for user growth. And a machine learning engineer at Facebook can work on fraud detection technology.
The average base salary for a data scientist at Meta is around $152,000, according to Glassdoor. As one of the highest-paid technology companies in the world, it’s not uncommon for data science professionals to earn over $200,000 on Meta.
Airbnb data scientists work on a variety of products and features, from fraud prevention to helping set rental prices. As the company grows, so does its demand for data science professionals. Currently, Airbnb has dozens of openings for data scientists, machine learning engineers, and analysts.
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With over 900 million active iPhones worldwide, Apple is a data collection giant. The company’s data science professionals work across all of its divisions, including hardware, software, music and entertainment services.
Data scientists working for the consulting firm Booz Allen are exposed to a range of customers and challenges, from helping commercial customers improve their cybersecurity to helping military customers.
Microsoft’s size, reach, and product diversity mean that its data scientists solve a wide range of problems, from improving Microsoft Cloud to avoiding biases in its language technologies.
The number of tools a data science professional masters can affect their pay, according to O’Reilly’s Data Science Research. Some salary increase toolkits include Apache Spark, Scala, and D3, which can add over $15,000 to a compensation package. Familiarity with cloud computing will also boost salaries, with survey respondents using Amazon Elastic MapReduce receiving a salary increase of around $6,000.
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O’Reilly also noted that those who used 15 or more tools could earn up to $30,000 more than those who used just 10 to 14. There is a premium associated with mastering a variety of tools.
Where you choose to work can determine your salary.
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