Amazon Web Services (AWS), Worldwide Public Sector (WWPS), MVP Analytics & Data Engineering Team (MADE) is seeking a Data Engineer who will be responsible for developing and managing big data systems. The ideal candidate will possess a deep understanding of data engineering fundamentals and best practices, with the skills and experience to develop autonomous, scalable programs. Familiarity and exposure to data warehousing and cloud-based service offerings are also requirements to raise the bar in this role.

The Data Engineer will be responsible for scaling existing infrastructure, incorporating new data sources, and building robust data pipelines for production level systems. In partnership with business intelligence engineers and analysts, the Data Engineer will work backwards from the customer to build reliable and scalable data solutions to meet the business needs.

Finally, this individual will work closely with project management teams to ensure proper guidance, use cases, and documentation is available to our Sales and Operations teams.

This position requires that the candidate selected be a US Citizen and must currently possess and maintain an active TS/SCI security clearance with polygraph.

Key job responsibilities
Analyze existing databases and warehouse structures to determine the most efficient and scalable methods for consolidating and aggregating data.

Design and develop all components of data solutions using Amazon Redshift and Amazon Aurora Postgres to create an Operations data warehouse.

Create and propose technical design documentation, including:
- Current and future ETL functionality
- Affected database objects
- Specifications, data flows, and diagrams detailing the proposed implementation

Design, develop, implement, test, document, and operate large-scale, high-volume, high-performance data structures for business intelligence analytics.

Implement data structures using best practices in data modeling to enable online reporting and analysis, leveraging:
- Business intelligence tools
- A logical abstraction layer for large, multi-dimensional datasets from multiple sources

Evaluate and provide feedback on dataset implementations designed and proposed by peer data engineers.

Manage AWS resources, including EC2, Redshift, S3, and others, while:
- Exploring and adopting the latest AWS technologies to enhance capabilities and efficiency

Participate in the full development lifecycle—from design, implementation, and testing to documentation, delivery, support, and maintenance—to produce comprehensive, usable dataset documentation and metadata.

Translate internal customer business requirements into repeatable, maintainable automated data engineering processes


A day in the life
About AWS
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.