About the job
Do you get excited about working with hyper scale data applications? Interested in security? Enjoy working with the latest and greatest AWS technologies? Ready to pioneer our data analysis, dashboarding, and storage solutions for a pipeline managing petabytes of data to transform the efficiency of our systems and customers? Here in Defensive Security, we have that special role just for you! The Vulnerability Management and Remediation team is building the next generation of mission critical data processing pipelines and applications. Our innovations handle petabyte scale data, in real time. These applications enable Amazon defensive security and security management teams globally. Our customers are Amazon, internal stakeholders, and Amazon’s customers. Our engineers work directly with stakeholders, get close to customers' needs, and are empowered to take technical risks to build the best-in-class software with respect to quality, scale, and insights. The technical foundation this team builds, helps keep all of Amazon (Fulfillment Centers, Physical Stores, Whole Foods, Fresh, etc.) secure.
Responsibilities
Design, develop, and maintain scalable data infrastructure and ETL pipelines to support business intelligence initiatives using automated dashboard solutions
Partner with Technical, Business, Operation, and Security teams to understand data requirements and design scalable and user-friendly BI solutions
Design and implement AI/LLM-powered solutions to automate human-in-the-loop tasks
Design and develop statistical analysis to support business decision making using the AI infrastructure and company wide solutions
Manage and execute entire project from start to finish including problem solving, data gathering and manipulation, predictive modeling and project management
Qualifications
Minimum
3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
Experience with data visualization using Tableau, Quicksight, or similar tools
Experience with data modeling, warehousing and building ETL pipelines
Experience in Statistical Analysis packages such as R, SAS and Matlab
Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
Preferred
Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets