Business Intelligence Engineer II, Retail Business Service Data Engineering Team
Amazon
Business Intelligence Engineer II, Retail Business Service Data Engineering Team
Job Description
Retail Business Services (RBS) supports Amazon’s Retail business growth WW through three core tasks. These are (a) Selection, where RBS sources, creates and enrich ASINs to drive GMS growth; (b) Defect Elimination: where RBS resolves inbound supply chain defects and develops root cause fixes to improve free cash flow and (c) supports operational process for WW Retail teams. Our team of high caliber software developers, applied scientists, data engineers, product managers and Business Intelligence Engineers use rigorous ML and deep learning approaches to ensure that we identify & fix the right catalog defect to ensure the good shopping experience for our customers. We are looking for a customer-obsessed Business Intel Engineer that thrives in a culture of data-driven decision making who will be responsible to help us hold a high bar for RBS Data Engineering Team This individual will be responsible for driving/creating: · Experience working with large, multi-dimensional datasets from multiple sources · Make recommendations for new metrics, techniques, and strategies to improve the operational and quality metrics. · Proficient using at least one data visualization product (Tableau, Qlik, Amazon QuickSight, Power BI, etc.) · Experience in deployment of Machine Learning and Statistical models · Building new Python utilities and maintaining existing ones · Enabling more efficient adhoc queries & analysis · Working closely with research scientists, business analysts and product leads to scale data · Ensuring consistency between various platform, operational, and analytic data sources to enable faster and more efficient detection and resolution of issues · Exploring and learn the latest AWS technologies to provide new capabilities and increase efficiencies · Mentoring the team on analytics best practices
Locations
- Chennai, Tamil Nadu, India
Skills Required
- data visualization using Tableauintermediate
- data modelingintermediate
- Statistical Analysis packages such as Rintermediate
- SQL to pull data from a databaseintermediate
- SQLintermediate
- Python scripting to process data for modelingintermediate
- Microsoft Excel at an advanced levelintermediate
- AWS solutions such as EC2intermediate
- data miningintermediate
Required Qualifications
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience (experience, 3 years)
- 2+ years of Tableau Desktop, Quicksight or other relevant data visualization software experience (experience, 2 years)
- Bachelor's degree or above in business administration, finance, economics, computer science, data science, engineering, or other related field, or 2+ years of Amazon RME (BB/3P) Full Time Exempt experience (experience, 2 years)
- Experience with data visualization using Tableau, Quicksight, or similar tools (experience)
- Experience with data modeling, warehousing and building ETL pipelines (experience)
- Experience in Statistical Analysis packages such as R, SAS and Matlab (experience)
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling (experience)
- Experience using SQL (Structured Query Language) to pull data from a database or data warehouse (experience)
- Experience using Python scripting to process data for modeling (experience)
Preferred Qualifications
- Master's degree or above in BI, finance, engineering, statistics, computer science, mathematics or equivalent quantitative field (degree in above in bi)
- Knowledge of Microsoft Excel at an advanced level, including: pivot tables, macros, index/match, vlookup, VBA, data links, etc. (experience)
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift (experience)
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets (experience)
- Experience developing and presenting recommendations of new metrics allowing better understanding of the performance of the business (experience)
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