Business Intelligence Engineer, EU Prime and Marketing Analytics & Science (PRIMAS)

Business Intelligence Engineer, EU Prime and Marketing Analytics & Science (PRIMAS)

Are you passionate about giving customers the richest, most inspiring experience in their shopping journey? Do you like to dive deep to understand how customer-centric solutions drive measurable results? Do you enjoy working closely with the business, scientists and software engineers to build scalable products? You are in the right place! Come join our Prime & Marketing Analytics and Science (PRIMAS) team, where your actions will have direct impact to millions of customers!

The EU Marketing & Prime organization is looking for a Business Intelligence Engineer. This role combines large-scale analytics with statistical and data-science work — causal inference, experimentation, and predictive modeling. If your background is in data science, applied statistics, or quantitative analytics, this is that work under Amazon's internal job title.

The PRIMAS team provides a comprehensive understanding of customer segments, affinities and lifetime value. We use the latest data science tools and advanced analytical techniques to study customer purchase and engagement behaviors and generate actionable insights on where, when and how we provide products and programs to our customers to meet their needs and to delight them. We help to increase customer engagement, sales and marketing efficiency. Our systems are built entirely in-house and on automated large-scale analytics systems. You will generate insights, design/deliver/measure experiments and strategies across marketing channels (SEM/SEO, Affiliates, Display, Social, Mobile, Email, Onsite, etc.), engagement products and customer segments. You will improve our understanding of customer behavior and engagement, design and conduct rigorous experiments on the effectiveness and efficiency of different marketing actions, and build the causal and conversion models that value and target our marketing. You will work on the forefront of consumer analytics, tackling some of the hardest problems in the industry alongside some of the best scientists, statisticians, and software engineers in the field.



Key job responsibilities
- Working on complex, loosely defined analytics problems and defining the team's Business Intelligence (BI) strategy — delivering independently and influencing the organization's BI architecture.
- Providing analytics solutions for complex business problems; building analyses and solutions that are robust, extensible and scalable; communicating effectively with management audiences (narratives, inputs into Business Reviews); refining BI strategies that cross teams and making technical trade-offs for long- and short-term needs.
- Designing and implementing technical solutions with an appropriate analytics strategy and data set design; understanding system limitations, scaling factors, boundary conditions, and the reasons for technical decisions.

- Building and evaluating causal and treatment-effect models that produce conversion and valuation signals consumed by downstream bidding and budgeting systems.
- Designing and running experiments and quasi-experiments (A/B, geo, holdout) to measure marketing incrementality, and developing measurement frameworks that quantify the true, platform-independent contribution of marketing over time.
- Working with first- and third-party data to identify and target high-opportunity customer segments where standard platform optimization falls short.
- Providing analyses, frameworks and solutions that inform multiple teams' business decisions and highlight new opportunities.
- Driving best practices in operational excellence, data modelling, and analysis.

Basic Qualifications

- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience in the data/BI space

Preferred Qualifications

- 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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