Sr Business Intelligence Engineer, GSO

1 month ago

Seattle, United States Amazon Full time

We are building the next generation of sales products and tools to accelerate growth at AWS. The AWS Sales Insights, Analytics, and Data Engineering & Science (SIADS) team is looking for a Sr Business Intelligence Engineer to support net-new insights products using external intelligence (3P data). You will leverage your exceptional analytical abilities, expertise in business intelligence tools, and passion for data-driven decision-making to drive business impact.

Key job responsibilities
- Architect and develop scalable and resilient data solutions for real-time and batch processing, enabling self-service business intelligence for analytics users.
- Build ETL pipelines, statistical models, and machine learning solutions using large, multidimensional datasets to uncover trends, patterns, and opportunities.
- Collaborate with product managers, engineers, data scientists, and business stakeholders to understand strategies, goals, and objectives, and align the analytics roadmap accordingly.
- Design and implement end-to-end reporting solutions, metrics, dashboards, and automated processes to drive key business decisions and track progress.
- Perform complex analyses to inform product strategy, design, and business priorities, presenting insights and recommendations to senior leadership.
- Continuously improve reporting and processes, automating and scaling solutions while ensuring stability and performance.
- Identify opportunities for new metrics, techniques, and strategies to enhance targeting, measurement, and overall product capabilities.
- Stay up-to-date with industry trends and best practices, contributing to the team's evolution through code reviews, design discussions, and knowledge sharing.

GSO2024

About the team
About AWS

Diverse Experiences
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 (gender diversity) 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.

About Sales, Marketing and Global Services (SMGS)

AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.

- 5+ years of professional or military experience
- Experience programming to extract, transform and clean large (multi-TB) data sets
- 5+ years of SQL experience and experience in scripting for automation (e.g. Python)
- Proficient in the theory and practical application of experimental design, statistical result analysis, information retrieval, data science, machine learning, and data mining

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