Amazon Selection and Catalog Systems (ASCS) builds the systems that host and run the comprehensive e-commerce product catalog. We power the online shopping experience for customers worldwide, enabling them to find, discover, and purchase anything they desire. Our scaled, distributed systems process hundreds of millions of updates across billions of products, including physical, digital, and service offerings.
You will be part of Catalog Support Programs (CSP) team under Catalog Support Operations (CSO) in ASCS Org. CSP provides program management, technical support, and strategic initiatives to enhance the customer experience, owning the implementation of business logic and configurations for ASCS. We are establishing a new centralized Business Intelligence team to build self-service analytical products for ASCS that provide relevant insights and data deep dives across the business. By leveraging advanced analytics and AI/ML, we will transform catalog data into predictive insights, helping prevent customer issues before they arise. Real-time intelligence will support proactive decision-making, enabling faster, data-driven decisions across the organization and driving long-term growth and an enhanced customer experience.
We are looking for a creative and goal-oriented BI Engineer to join our team to harness the full potential of data-driven insights to make informed decisions, identify business opportunities and drive business growth.
This role requires an individual with excellent analytical abilities, knowledge of business intelligence solutions, as well as business acumen and the ability to work with various tech/product teams across ASCS. This BI Engineer will support ASCS org by owning complex reporting and automating reporting solutions, and ultimately provide insights and drivers for decision making.
You must be a self-starter and be able to learn on the go. You should have excellent written and verbal communication skills to be able to work with business owners to develop and define key business questions, and to build data sets that answer those questions.
As a Business Intelligence Engineer in the CSP team, you will be responsible for analyzing petabytes of data to identify business trends and points of customer friction, and developing scalable solutions to enhance customer experience and safety. You will work closely with internal stakeholders to define key performance indicators (KPIs), implement them into dashboards and reports, and present insights in a concise and effective manner. This role will involve collaborating with business and tech leaders within ASCS and cross-functional teams to solve problems, create operational efficiencies, and deliver against high organizational standards. You should be able to apply a breadth of tools, data sources, and analytical techniques to answer a wide range of high-impact business questions and proactively uncover new insights that drive decision-making by senior leadership. As a key member of the CSP team, you will continually raise the bar on both quality and performance. You will bring innovation, a strategic perspective, a passionate voice, and an ability to prioritize and execute on a fast-moving set of priorities, competitive pressures, and operational initiatives. There will be a steep learning curve, adding a fair amount of business skills to the individual.
Key job responsibilities
- Work closely with BIEs, Data Engineers, and Scientists in the team to collaborate effectively with product managers and create scalable solutions for business problems
- Create program goals and related metrics, track progress, and manage through obstacles to help the team achieve objectives
- Identify opportunities for improvement or automation in existing data processes and lead the changes using business acumen and data handling skills
- Ensure best practices on data integrity, design, testing, implementation, documentation, and knowledge sharing
- Contribute to supplier operations strategy development based on data analysis
- Lead strategic projects to formalize and scale organizational processes
- Build and manage weekly, monthly, and quarterly business review metrics
- Build data reports and dashboards using SQL, Excel, and other tools to improve business efficiency across programs
- Understand loosely defined or structured problems and provide BI solutions for difficult problems, delivering large-scale BI solutions
- Provide solutions that drive the team's business decisions and highlight new opportunities
- Improve code quality and optimize BI processes
- Demonstrate proficiency in a scripting language, data modeling, data pipeline design, and applying basic statistical methods (e.g., regression) for difficult business problems
A day in the life
A day in the life of a BIE-II will include:
- Working closely with cross-functional teams including Product/Program Managers, Software Development Managers, Applied/Research/Data Scientists, and Software Developers
- Building dashboards, performing root cause analysis, and sharing actionable insights with stakeholders to enable data-informed decision making
- Leading reporting and analytics initiatives to drive data-informed decision making
- Designing, developing, and maintaining ETL processes and data visualization dashboards using Amazon QuickSight
- Transforming complex business requirements into actionable analytics solutions.
About the team
This central BIE team within ASCS will be responsible for building a structured analytical data layer, bringing in BI discipline by defining metrics in a standardized way and establishing a single definition of metrics across the catalog ecosystem. They will also identify clear sources of truth for critical data. The team will build and maintain the data pipelines for critical projects tailored to the needs of ASCS teams, leveraging catalog data to provide a unified view of product information. This will support real-time decision-making and empower teams to make data-driven decisions quickly, driving innovation. This team will leverage advanced analytics that can shift us to a proactive, data-driven approach, enabling informed decisions that drive growth and enhance the customer experience. This team will adopt best practices, standardize metrics, and continuously iterate on queries and data sets as they evolve. Automated quality controls and real-time monitoring will ensure consistent data quality across the organization.
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