Sr Business Intelligence Engineer, Global Talent Management and Compensation (GTMC)

- 10+ years of professional or military experience
- 5+ years of SQL experience
- Experience programming to extract, transform and clean large (multi-TB) data sets
- Experience with theory and practice of design of experiments and statistical analysis of results
- Experience with AWS technologies
- Experience in scripting for automation (e.g. Python) and advanced SQL skills.
- Experience with theory and practice of information retrieval, data science, machine learning and data mining
- Experience working directly with business stakeholders to translate between data and business needs
- Experience managing, analyzing and communicating results to senior leadership

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 other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $117,300/year in our lowest geographic market up to $202,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Global Talent Management and Compensation (GTMC) is centrally responsible for creating and evolving Amazon’s talent programs and processes. The Modeling Analytics and Research Science (MARS) team within the GTMC Science, Insights, Engineering Research, Reporting and Analytics (SIERRA) team is a team of Research Scientists, Data Scientists, and BIEs with a direct impact on 300,000+ Amazonians across all of our businesses and locations around the world. We use employee data to drive the right conversations with Amazon’s Senior Vice President of PXT (People eXperience and Technology) and other executives about how to improve existing talent programs and explore new and developing questions that shape our talent landscape.

MARS is looking for experienced analysts to support the end-to-end business intelligence for new and evolving talent products, proprietary to Amazon. In this role, you will work directly with customers, leaders, and program owners, influencing opportunity identification, product design, data capture, reporting/measurement, and evaluation strategies. You will use existing data/research to identify new challenges and opportunities, as well as data collected through methods you create/define. You will leverage your subject matter expertise and strong written/verbal communications skills to develop reproducible research/analyses shared with PXT analysts embedded across Amazon’s diverse set of businesses. As the end-to-end analyst for the product, you will also build proof-of-concept reporting tools, while scaling ad hoc data access, enabling the product team self-serve and make informed decisions in a fast-paced, ambiguous business environment.


Key job responsibilities
· Align with leaders and customers to translate business issues into testable hypotheses answerable with data
· Develop and execute analyses to drive talent insights
· Create data capture and measurement strategies that drive business action
· Drive a roadmap from descriptive to predictive analytics, moving routine tasks to self-service for your client group
· Work closely with peers and stakeholders to access, pull, transform, and analyze data from a variety of PXT sources
· Gather new data, and/or creatively use existing data to propose metrics and present them in compelling but easily understood way to a non-statistical audience.
· Apply statistical tests to data, including (but not limited to), t-tests, chi-square, and regression.
· Compile results in a concise, meaningful, and actionable format and share findings to senior leadership
· Build processes to scale fast and accurate on-demand and ad hoc data/analytical requests for leadership and key stakeholders