Data Engineer II, SPT Analytics

As we strive to be Earth's most customer-centric company, Amazon has reinvented how hundreds of millions of people shop online – providing customers with the opportunity to find and discover virtually anything they want to buy and providing millions of sellers with a platform for growing successful businesses. We are looking for an exceptional data engineer to help us develop new ways to build trust and loyalty with sellers, a crucial component of our flywheel

Sellers’ trust in Amazon is our top priority and in this role, you will be tasked with building that trust over time by building and maintaining the data sources that power all of our decision making. Amazon’s growth requires leaders who move fast, have an entrepreneurial spirit to create new solutions, have an unrelenting tenacity to get things done, and are capable of breaking down and solving complex problems.

The successful candidate will be a self-starter, comfortable with ambiguity and be able to create and maintain efficient & automated processes. They know and love working with data engineering tools, can model multidimensional datasets, and can partner effectively with business leaders to build the right data pipelines to answer key business questions. They will build efficient, flexible, extensible, and scalable data models, ETL designs and data integration services. They will also be required to support and manage growth of these data solutions. They are analytical and creative, and don’t quit. This is a role with high visibility to senior leadership and with high opportunity for impact for those willing to roll up their sleeves and dive deep to achieve results.

Key job responsibilities

• Design, implement, and maintain complex data pipelines and ETL / ELT processes to support analytical and operational needs
• Architect and develop scalable, high-performance data systems using cloud technologies and big data platforms
• Manage and optimize Redshift cluster performance, including WLM, vacuum operations, and table maintenance
• Design and implement optimal table distribution styles and sort keys for Redshift tables to maximize query performance
• Monitor and tune Redshift query performance, including analyzing query plans and implementing performance optimization strategies
• Collaborate with data scientists, analysts, and business stakeholders to gather requirements and implement appropriate data solutions
• Create and optimize data models to support both ad-hoc and pre-built reporting needs
• Implement comprehensive data quality frameworks and monitoring systems to ensure data integrity and reliability
• Design and maintain metadata management systems and documentation
• Provide technical leadership and mentorship to junior engineers on data engineering best practices
• Evaluate and recommend new technologies to enhance data infrastructure capabilities
• Troubleshoot and resolve complex data-related issues in production environments
• Contribute to data governance policies and procedures
• Optimize query performance and data storage solutions for improved efficiency and cost-effectiveness
• Participate in the full development lifecycle from design through implementation, testing, and maintenance

BASIC QUALIFICATIONS

PREFERRED QUALIFICATIONS

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