Business Intelligence Engineer | Physical Stores

Job description

DESCRIPTION

At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. With the growth and expansion of our physical stores business, we are looking for a Sr. BI Engineer to help us uncover key insights and drive automation for store operations, labor planning and other areas. This role is both creative and data driven – we need someone with data mining and modeling skills who is comfortable facilitating ideation and working from concept through to execution, as well as help us think big about options for long-term automation.

This position requires the ability to build tools and support structures needed to analyze data, have a great business sense, and the desire to influence key strategic decisions with data-modeled analysis. Working with engineering and product teams and collaborating with key business stakeholders across our business, you will have the opportunity to design and implement features to enhance the experience of Amazon customers, and internal teams. BIEs at Amazon work directly with diverse teams including Product Managers, Data Engineers and other functions. In this role, you will have an opportunity to work on mathematical problems with a large element of unpredictability. You will analyze and process large amounts of data, improve existing approaches and develop new database implementations, statistical models and data visualizations.

This is a highly visible role with direct, ongoing impact to Amazon’s physical stores business and our customer experience.

The successful candidate will:
· Interface with business customers, gathering requirements and delivering complete BI solutions.
· Collaborate closely with business teams to dive deep into hard technical problems and develop insightful solutions that enable improvements in accuracy, productivity and customer experience.
· Design and develop complex tables to automate our existing processes, and improve on these processes using new mathematical, simulation and optimization models.
· Prototype these models by using modeling languages such as R, or in software languages such as Python.
· Partner with data and software engineering teams to drive scalable implementations.
· Develop and improve forecasting methodologies by applying time series models (e.g. ARIMA) or machine learning algorithms.
· Develop queries and Tableau visualizations for ad-hoc requests and projects, as well as ongoing reporting.
· Own analytical tools and reporting for Forecasting and other functional areas.
· Monitor and troubleshoot operational or data issues in the data pipelines, and review and audit existing ETL jobs and SQL queries.

PREFERRED QUALIFICATIONS

· Masters degree is a plus
· Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
· Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives
· Previous experience in a Machine Learning or data scientist role with a technology company

Desired profile

BASIC QUALIFICATIONS

BASIC QUALIFICATIONS
· BA/BS in Math/Statistics/Engineering or other equivalent quantitative discipline
· 3+ years in relevant experience as data scientist, software engineer, business intelligence engineer, or equivalent
· 2+ years of hands-on experience writing complex, highly-optimized SQL queries across large datasets.
· Experience with stats software (R, Python) or other domain specific software.
· Demonstrated development and application of predictive statistical procedures in languages such as R, SAS, SPS.
· Experience independently leading and delivering end-to-end analytics analyses and products.
· Specific demand forecasting experience preferred (time series analysis, ML forecasting algorithms, etc.).
· Working knowledge of visualization tools (primarily Tableau).
· Knowledge of scripting for automation (e.g. Python, Perl, Ruby).
· Advanced Excel skills.
· Experience conveying mathematical concepts and considerations to non-experts.