Business Intelligence Engineer, Prime Video, 3rd Party Device Partnerships
Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?
Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events.We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide.
The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do.
The 3rd Party Device Partnerships (3PDP) team manages the business, product, and technical relationship to distribute Prime Video apps to customer devices (including Smart TVs, Games Console, Pay TV operators and mobile devices). We (i) ensure the Prime Video application has broad distribution globally, (ii) manage marketing relationships with device partners to drive promotion for our top titles, and (iii) design and scale features to improve discovery and engagement, and deliver the best version of our app on each device.
We’re looking for a BIE who can help us drive data-driven decision-making across Prime Video 3PDP. You will be responsible for executing the Prime Video marketing analytics strategy for 3rd Party Living Room devices. You will leverage your technical and analytical skills to create scalable data structures, develop automated data visualization tools, and drive strategic insights on how to grow the business through targeted marketing decisions.
As a BIE, you will own partnering with data engineering and software engineering teams to drive best-in-class business intelligence solutions at scale. You will work with structured and unstructured data sources to develop deep business insights and architect key business metrics.You will partner with scientists to build forward-looking data experimentation, predictive modeling, and machine learning algorithms. The role is cross functional and will require working closely with a wide variety of stakeholders including senior members across engineering, product, business leadership, and economics/data science teams.
Key job responsibilities- Drive the design, development, and maintenance of innovative and scalable intelligence solutions across the organization, with the highest standards of analytical rigor and data integrity.
- Develop domain expertise and drive major contributions to our data and research roadmap. Leverage SQL, ETL, Redshift, and other available tools to uncover deep business insights, trends, and opportunities to innovate on behalf our customers.
- Partner with data engineering to build best-in-class data architecture and pipelines to support analytics needs.
- Translate ambiguous business problem statements into actionable analytical solutions. Manage large scale data requests and projects to define requirements, manage timelines, and coordinate activities with other involved team members.
- Partner with scientists to translate business insights and trends into experimentation and testing, and modeling.- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- 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
- Experience with SQL
- Experience in the data/BI space- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
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