Data Engineer, Central InfraOps Analytics Team

Data Engineer

As a Data Engineer you will enable data-driven decision making within the Amazon Web Services Data Center Infrastructure Operations organization. The Infrastructure Operations Team is responsible for planning, implementing, monitoring and continuously improving the global Amazon Data Center infrastructure. The team supports all aspects of the Data Center based organizations, including but not limited to: Safety, Security, maintenance, operations, logistics, engineering and equipment management.

Key job responsibilities:

• Design, develop, and maintain ETL pipelines to ingest data into the data warehouse and data lake
• Create and optimize logical data models that drive physical design for the Infrastructure Operations organization
• Implement data quality measures and ongoing monitoring to ensure data integrity
• Build scalable, efficient, and maintainable data solutions that support business intelligence needs
• Optimize data storage and query performance across various data platforms
• Develop automated processes to replace manual data operations
• Collaborate with business stakeholders to understand data and reporting requirements
• Translate business questions into data solutions that drive decision-making
• Mentor and develop peers in data engineering best practices
• Participate in code reviews, design discussions, and team planning
• Improve self-service access to data for business users
• Enhance code quality and dependency management
• Automate manual processes to increase efficiency
• Identify and resolve root causes of complex data problems

A day in the life:

At AWS, the Data Engineer fully embraces the "You Build It, You Own It" philosophy, taking complete ownership of data solutions from conception through deployment and ongoing maintenance. You design architectures, implement pipelines, and remain responsible for their health and evolution as business needs change. Each day begins with reviewing pipeline alerts and data quality metrics, followed by a 15-30 minute team stand-up to align on priorities and discuss blockers. You'll spend time monitoring infrastructure, reviewing logs for ETL pipeline health and data lake performance, then dedicate time to address stakeholder queries and prioritizing incoming requests via email, Slack and intake forms. The majority of your time is spent developing and maintaining ETL pipelines that ingest infrastructure operational data from global data centers, which includes writing code, debugging issues, optimizing queries, and implementing quality checks. The role requires frequent context switching between developing new data models, supporting existing infrastructure, and consulting on data utilization.

Key challenges you'll tackle include unifying and understanding fragmented data from diverse data center systems, enabling infrastructure monitoring, supporting analytics for capacity planning, driving optimization through data insights, automating manual processes, creating self-service access for business users, maintaining quality across massive datasets, ensuring compliance with strict security requirements, designing for scale as AWS expands globally, and modernizing legacy systems to reduce technical debt.