DESCRIPTION Amazon Advertising is looking for a motivated Business Intelligence Engineer with strong technological, analytical, and business intelligence skill sets and experience to join the Advertising Product Finance team supporting the Audio and Devices advertising businesses.
This position will be responsible to develop and managing large, complex datasets that are both scalable and easily accessible. This candidate will be comfortable with ambiguity and a self-starter who seeks requirements for major business initiatives, and will be an essential team member that can articulate insights using the data.
The candidate should enjoy diving into data, automating or building new data-driven reporting mechanisms, and recommending or implementing solutions to facilitate financial and business metrics reporting.
As you further your career as a Business Intelligence Engineer at Amazon, you will focus on improving corporate reporting frameworks and data visualization. You will analyze performance data, discover and solve real-world problems and build metrics and business cases to improve decision making. You can expect to leverage your analytical skills and have full ownership of your projects.
Key job responsibilities
As a Business Intelligence Engineer, you create insightful recommendations by applying your data science, analytical and business skills to complex problems. You use will use insights to challenge, guide and illuminate our product strategy, which directly influences what we build for customers. Data-driven decision-making is at the core of Amazon's culture and this role will have a direct impact on the decisions and strategy of the Finance Data Analytics team.
- Design, implement, and support a platform providing secured access to large datasets
- Interface with Business, Finance and Accounting customers, gathering requirements and delivering end-to-end BI solutions
- Model data and metadata to support ad hoc and pre-built reporting
- Own the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc. to drive key business decisions
- Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation
- Tune application and query performance using profiling tools and SQL
- Analyze and solve problems at their root, stepping back to understand the broader context
- Learn and understand a broad range of Amazon's data resources and know when, how, and which to use
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for datasets
- Triage many courses of action in a high-ambiguity environment, making use of both quantitative analysis and business judgment
A day in the life
- Work with stakeholders to understand and design solutions on major projects
- Seek opportunities to streamline existing Finance manual process and automate using BI technology
- Attend team standup to go over existing development, blockers and help troubleshoot issues
- Continue to adopt new technologies that benefits business. Examples: linear regression model, serverless automation, big data processing and system integrations.
About the team
The APM advertising product finance team directly supports Amazon Device, Audio, and Video advertising demand-side platform (DSP) product teams. We also act as internal partners to support and drive our ad sales and supply teams. Our focus is to accelerate top-line revenue and improve profitability by evaluating overall ad supply monetization and utilization, product feature or sales package adoption, campaign performance, and other various product drivers of growth.
We are open to hiring candidates to work out of one of the following locations:
New York, NY, USA | Seattle, WA, USA BASIC QUALIFICATIONS - 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
