Amazon Sr BIE Data eNgineering Analytics DNA 

United States, Texas, Austin 

920527687

Key job responsibilities
-Develop and drive best practices to align with our theme, Accuracy, Complete and Timely (ACT) information: data integrity, test design, analysis, validation, and documentation
-Mine data from database tables (ex. Redshift, MySQL, Oracle) using SQL and log files by writing scripts (ex. PERL, Python, UNIX)
-Build solutions to have maximum scale and self-service ability for stakeholders-Produce written recommendations and insights for key stakeholders that will help shape effective metric development and reporting
-Lead the design, implementation, and deployment of BI solutions for complex and ambiguous problems-Simplify and automate reporting, audits and other data-driven activities-Learn new technology and techniques to meaningfully support product and process innovation-To develop and drive best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation
-Learn new technology and techniques to meaningfully support product and process innovationA day in the life
In this role, you will transform complex data into actionable insights. Your data-driven solutions will influence strategic planning and inform daily decision-making. You will leverage a range of descriptive, diagnostic, predictive, and prescriptive analytics techniques to uncover insights and opportunities.

- 7+ years of SQL experience
- Experience in scripting for automation (e.g. Python) and advanced SQL skills.
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience working directly with business stakeholders to translate between data and business needs
- Experience building measures and metrics, and developing reporting solutions

- Knowledge of data warehousing and data modeling
- Experience with AWS technologies
- Experience programming to extract, transform and clean large (multi-TB) data sets
- Experience with operations/supply chain
- Experience with theory and practice of information retrieval, data science, machine learning and data mining