Amazon Senior Business Intelligence Engineer Salary
Amazon Senior Business Intelligence Engineer Salary – Amazon.com Free shipping on millions of items. Get the best of Shopping and Entertainment with Prime. Enjoy low prices and great deals on the largest selection of groceries and other products, including fashion, home, beauty, electronics, Alexa…
AWS is seeking a professional Senior Business Intelligence Engineer to drive our analytical revolution in Talent Acquisition (TA). You get the opportunity to work on rebuilding our analytical capabilities, from data entry, to complex business transformations to end-user reporting and beyond. In this role, you will create and build on behalf of the candidate, experiment and test new ideas, evangelize success, and encourage consistency.
Amazon Senior Business Intelligence Engineer Salary
The ideal candidate is an independent Business Intelligence Engineer who can acquire data, clean, analyze, refine, enrich, model, present, automate and document our business data path. You will always be looking for ways to optimize information flow processes, stay abreast of the latest trends in data warehousing and be able to coordinate and work on multiple related projects.
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Are you passionate about driving business & customer impact through careful analysis and data-driven insights? Are you a highly technical individual who enjoys working with customers to change the way businesses operate? Are you a builder who excels with ambiguity? Are you inspired by invention? Is problem solving through teamwork and working in a startup environment in your DNA? Do you like the idea of seeing how your work affects the bigger picture? Answer yes to all of these and you will fit in here. Basic Qualifications
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or any other legally protected status. For individuals with disabilities who wish to request accommodation, please visit https://www.amazon.jobs/en/disability/us.
Tags: AWS Business Intelligence Computer Science Data Analytics Data Channels Data Warehousing Engineering ETL Financial Finder Mathematics Pipeline Statistics SQL Tableau
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Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill , equipment and products used. O’Reilly this week published the results of his salary survey, which shows the median salary for AI and data professionals in the US and UK is $146,000 per year. While that’s certainly more than a lot of jobs, it only represents an average 2.25% annual increase rate, according to the survey, which also cuts salaries by education, job title, gender, state, programming language, and platform.
Among the 3,136 data and AI professionals (i.e. data scientists, data engineers, and AI and machine learning specialists) who took O’Reilly’s survey via the Internet in June, those from California earned the most, earning an average of $176 , 000 per year. That should come as no surprise, given the number of major tech companies making their home in Silicon Valley, as well as the state’s overall high cost of living. Massachusetts, Delaware, New Jersey, New York, and Maryland follow the Golden State, with median salaries of less than $150,000.
Men accounted for 81% of survey takers, and they reported significantly higher median salaries ($150,000) than women ($126,000). Women earn only 84% of what men earn, according to the survey. “The differences exist regardless of education,” O’Reilly said in the survey.
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However, despite the salary difference, the percentage of women who have degrees is higher than that of men, according to the survey, which shows 16% of women have doctorates, compared to 13% of men. And 47% of women have masters degrees, compared to 46% of men.”
The difference in salary between the sexes is also visible when viewed through the lens of the position. “At the executive level, the median salary for women is $163,000 versus $205,000 for men (20% difference),” O’Reilly said in the report. “At the director level, the difference is much smaller – $180,000 for women versus $184,000 for men – and women’s salaries [at the director level] are actually higher than at the executive level.”
O’Reilly also collects data on what programming languages AI and data professionals use. Unsurprisingly, the usual suspects are always present: Python (used by 61% of respondents), SQL (54%), JavaScript 32%), Bash (29%), Java (24%), and R (20%).
But when the Sebastopol, California-based media company compared language data with salary data, some interesting trends emerged. For example, the AI and data pro who ticked next to Rust had the highest average salary ($180,000+) of all languages. It was followed by Go ($179,000) and Scala ($178,000).
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O’Reilly tries to explain it: “The supply of talent for newer languages like Rust and Go is relatively small. While there may not be a huge demand for data scientists using this language (yet), there is definitely some demand—and with a shortage of experienced Go and Rust programmers, they earn higher salaries,” he said in the report.
Unsurprisingly, the most popular languages (Python, SQL, Bash, and R) are back to average in terms of salaries, with a bunch of them showing up at just under $150,000, just around the absolute average salary in this study. Java is showing slightly increasing traction, averaging a little over $150,000 (right ahead of everyone’s favorite language, “I don’t use a programming language”). Outliers on the downside include Perl, D, and CSS, which cluster around $125,000/year. level.
O’Reilly also asked about what tools and platforms AI and pro data are using for statistics and machine learning, and this is where salary comparisons get really interesting. For starters, it’s clear that people who use these tools are paid slightly more than the average respondent of this survey; that is, the distribution is skewed higher than $150,000.
Unsurprisingly, some of the most popular statistics and machine tools and platforms, such as PyTorch and TensorFlow, are grouped in the middle, which on this chart is around $155,000. Outliers at the high end include H2O.ai, where the average user is paid around $175,000, and KNIME, coming in just below that. Spark NLP, SparkMLlib, and Google Prediction round out the top five.
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The outlier on the lower end is Stata, with a median salary of around $115, 000 per year. There was a big jump then to IBM System ML and Nimble (a lightweight deep learning library based on PyTorch) for around $140,000, followed by Excel and everyone’s favorite ML/stats package “I don’t use any ML/stats tools” right at around the median for the entire survey (approximately $145,000).
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