Data Engineer , Amazon Customer Service

About the role

As Data Engineer on the Amazon Customer Service Data Intelligence team, you will design and implement robust data pipelines and infrastructure that power our organization's data-driven decisions and AI capabilities. This role is critical in developing and maintaining our enterprise-scale data processing systems that handle high-volume transactions while ensuring data security, privacy compliance, and optimal performance. You'll be part of a dynamic team that designs and implements comprehensive data solutions, from real-time processing architectures to secure storage solutions and privacy-compliant data access layers. The role involves close collaboration with cross-functional teams, including software development engineers, product managers and scientists, to create data products that power critical business capabilities. You'll have the opportunity to work with leading technologies in cloud computing, big data processing, and machine learning infrastructure, while contributing to the development of robust data governance frameworks.

If you're passionate about solving complex technical challenges in high-scale environments, thrive in a collaborative team setting, and want to make a lasting impact on our organization's data infrastructure, this role offers an exciting opportunity to shape the future of our data and AI capabilities.

Key job responsibilities

- Design and implement ETL/ELT frameworks that handle large-scale data operations, while building reusable components for data ingestion, transformation, and orchestration while ensuring data quality and reliability.

- Establish and maintain robust data governance standards by implementing comprehensive security controls, access management frameworks, and privacy-compliant architectures that safeguard sensitive information.

- Drive the implementation of data solutions, both real-time and batch, optimizing them for both analytical workloads and AI/ML applications.

- Lead technical design reviews and provide mentorship on data engineering best practices, identifying opportunities for architectural improvements and guiding the implementation of enhanced solutions.

- Build data quality frameworks with robust monitoring systems and validation processes to ensure data accuracy and reliability throughout the data lifecycle.

- Drive continuous improvement initiatives by evaluating and implementing new technologies and methodologies that enhance data infrastructure capabilities and operational efficiency.

A day in the life

The day often begins with a team huddle to align priorities, followed by a review of data pipeline monitoring alarms to address any processing issues and ensure data quality standards are maintained across systems. Throughout the day, you'll find yourself immersed in various technical tasks, including developing and optimizing ETL/ELT processes, implementing data governance controls, and reviewing code for data processing systems. You'll work closely with software engineers, scientists, and product managers, participating in technical design discussions and sharing your expertise in data architecture and engineering best practices. Your responsibilities extend to communicating with non-technical stakeholders, explaining data-related projects and their business impact.