Business Intelligence Analyst Google Salary
Business Intelligence Analyst Google Salary – Data science is a field that encompasses many disciplines. But all of these data science roles require strong analytical skills, technical knowledge, and the ability to glean actionable insights from a trove of data.
As more industries use innovative data applications—from the healthcare sector to using data science to improve patient care and drug development to Spotify using music data for personalized recommendations—data professionals of all stripes are in high demand. And there’s a plethora of different job opportunities that cater to a wide range of passions and interests.
Business Intelligence Analyst Google Salary
Data science occupations command high salaries because there is not enough supply (the profession has about 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 as, over the past few years, a growing number of organizations have begun to see the importance of data-driven decision making. Whether e-commerce companies are incorporating artificial intelligence or travel companies are incorporating machine learning into their platforms, data scientists are at the forefront of the latest technology revolution.
In the United States, data science professionals typically earn about $117,000 a year, according to Glassdoor. However, this can vary depending on a number of factors, including years of experience, education level, industry, location and area of specialization. For example, in big cities like New York or San Francisco, data scientists can earn as much as $180,000. Machine learning engineers often earn more than data analysts, and those who work in the consumer technology sector typically earn more than those who work for government agencies, nonprofits, or healthcare organizations.
The responsibilities of data scientists can vary from company to company. Generally speaking, data scientists source and process data available to a company.
The average annual salary for a data scientist in the US is around $117,000, according to Glassdoor.
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Data engineers build pipelines that funnel data from various sources. They focus on production readiness of raw data and components such as format, resilience, scaling, data storage and security.
The average annual salary for a data engineer in the US is about $113,000, according to Glassdoor.
Data architects envision and design an organization’s data management framework. They do this by evaluating an organization’s data sources, 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 US is about $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 US is around $106,000, according to Glassdoor.
Big data engineering focuses on the infrastructure that allows people to collect and organize large amounts of data. It makes billions of clicks, taps, likes, swipes, shares and purchases usable for data analysis. They do this by focusing on building data pipelines, designing and managing data infrastructure, managing data storage, and ETL (Extract, Transform, Load) processes.
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The average annual salary for a big data engineer in the US is about $125,000, according to Glassdoor.
A machine learning (ML) engineer is a programmer who designs self-driving software that uses data and automates predictive models. ML engineers bridge the gap between data and software, creating programs that allow machines to operate without direct human assistance.
The average annual salary for a machine learning engineer in the US is about $123,000, according to Glassdoor.
Machine learning is a subset of data science. Although machine learning engineers and AI engineers do some of the same work, artificial intelligence (AI) engineering focuses more on developing systems and machines that mimic human cognitive functions, making them more adept at solving problems.
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The average annual salary for an AI engineer in the US is about $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 company, replace hardware or software, and determine which products and services best support teams within an organization.
The average annual salary for an enterprise architect in the United States is about $164,000, according to Glassdoor.
Business intelligence (BI) analysts use their data to make decisions. They collaborate with the business team to understand their needs, review data, and write finance and market intelligence reports.
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Natural language processing engineers develop systems and devices that understand human language. A subset of artificial intelligence and machine learning, NLP engineers work at the intersection of computer science, information science, AI, and linguistics.
The average annual salary for an NLP engineer in the US is around $88,000, according to Glassdoor.
Database managers are often confused with database administrators, but their roles are different. By creating, updating and deleting the former database, a database manager maintains the results and performance of the database. Database managers also maintain the safety and security of an organization’s databases.
The average annual salary for a database manager in the United States is about $76,000, according to Glassdoor.
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The average annual salary for a database developer in the United States is about $97,000, according to Glassdoor.
A stepping stone to a data science career, data analysts identify trends, patterns, and relationships in data to inform business decisions. Data analysts mine data, organize insightful findings, create visualizations and present reports to stakeholders.
– Data cleaning to rid data of errors and discrepancies and gather valuable insights by identifying patterns, relationships and trends
– Familiarity with data analytics 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 US is about $74,000, according to Glassdoor.
The more a company relies on data, the more it pays its data scientists. Some of the highest paying data science companies are social networking sites (which collect user data), e-commerce platforms (which track billions of data points on consumer behavior) and real-time services (which rely on data to respond to customers).
Amazon’s data scientists work across all its verticals, from parsing consumer purchase data on its e-commerce platform to analyzing user behavior for its advertising department.
The average base salary for an Amazon data scientist is about $137,000, according to Glassdoor, although Amazon employees often earn much more. In 2022, Amazon raised its base pay salary cap for corporate and technology workers to $350,000, citing competition for tech talent.
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Uber’s data scientists work in product analytics (such as ensuring the accuracy of map and geolocation technology), finance and operations (such as using data to help the company with financial planning and competitive strategy), and marketing (such as helping to generate analytical insights). drawing for) growth, brand awareness, customer retention, and improve engagement).
Mater data scientists work on products like Facebook, Instagram, Oculus, Messenger, and WhatsApp. A data scientist focused on Instagram can help its monetization department drive long-term strategies for revenue sharing. A business analyst at WhatsApp can collaborate with sales and marketing teams to identify opportunities for user growth. And a machine learning engineer at Facebook might work on fraud detection technology.
The average base salary for a data scientist at Meta is about $152,000, according to Glassdoor. As one of the highest paying tech companies in the world, it’s not uncommon for data science professionals to earn upwards of $200,000 per month.
Airbnb’s data scientists work on a variety of products and features, from fraud prevention to helping determine rental prices. As the company grows, so does its demand for data science professionals. Airbnb currently has dozens of open roles for data scientists, machine learning engineers and analysts.
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With more than 900 million active iPhones in the world, Apple is a data collection juggernaut. The company’s data science professionals work across all its divisions, including hardware, software, music and entertainment services.
Data scientists working for consulting firm Booz Allen face a wide variety of clients and challenges, from helping commercial clients improve their cybersecurity to assisting military clients.
Microsoft’s size, reach and product diversity mean its data scientists can solve a wide range of problems, from improving the Microsoft cloud to preventing bias in its language technology.
According to O’Reilly’s Data Science Survey, the tools a data science professional has mastered can affect their compensation. Some salary-boosting toolsets include Apache Spark, Scala, and D3, which can add upwards of $15,000 to a compensation package. Familiarity with cloud computing will also increase salary, with survey respondents who use Amazon Elastic MapReduce seeing a nearly $6,000 salary increase.
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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 10 to 14. There is a premium attached to mastering a variety of tools.
Where you choose to work can determine your salary.
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