Job summary
Location: Sunnyvale, CA | Palo Alto, CA | Cupertino, CA | San Francisco, CA
EC2 Capacity Intelligence & Enablement team is looking for a customer-obsessed Business Intelligence Engineer to join the team. AWS has a culture of data-driven decision making, and requires business intelligence that is timely, accurate and actionable. This role will partner closely with the business teams to understand the data-needs of the team and ensure that the business teams are able to answer hard questions with real data. This individual will participate in business reviews and new-initiatives. They should be able to identify and help close gaps in the information available, as well as predict up-and-coming data-needs when participating in discussions about new products or features.
You should be highly collaborative and organized. You should be capable of prioritizing multiple deliverables and effectively communicate progress and blockers. You should feel comfortable working across multiple data-sources, and be capable of participating in and understanding how multiple systems communicate information to one-another.
Key job responsibilities
· Owning the design, development, and maintenance of scalable solutions for ongoing metrics, reports, analyses, dashboards, etc. to support analytical and business needs.
· Translate basic business problem statements into analysis requirements.
· Use analytical and statistical rigor to answer business questions and drive business decisions.
· Find and create ways to measure the customer experience to drive business outcomes.
· Develop queries and visualizations for ad-hoc requests and projects, as well as ongoing reporting.
· Write queries and output efficiently, and have in-depth knowledge of the data available in area of expertise. Pull the data needed with standard query syntax; periodically identify more advanced methods of query optimization. Convert data to make it analysis-ready.
· Recognize and adopt best practices in reporting and analysis: data integrity, design, analysis, validation, and documentation.
· Troubleshoot operational data-quality issues.
· Review and audit existing ETL jobs and queries.
· Recommending improvements to back-end data sources for increased accuracy and simplicity.
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