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Amazon
AOP (Analytics Operations and Programs) team powers the Data and Insights for ROW Operations. Team is responsible for development of business-critical BI and analytics solutions for Operations transcending all miles (barring Supply chain).
This role is the single-threaded leader responsible for building reporting and analytics capabilities for First-Mile and Middle-mile for IN and ECCF (AU, BR, MX, AMET, SG) and lead BI Artefacts & Practice Standardization across ROW marketplaces
Key job responsibilities
The incumbent is responsible to enable Operations teams meet their respective S-team/VP goals and charters. The incumbent is a trusted-partner to respective Operations teams by sharing business-leading insights and prescriptive actions - requiring the incumbent to be fully-aware of Org-level strategic direction, key priorities and challenges [LP Indexed: Earn trust].
Primary objective of this role is to provide visibility on Operations performance and enable Operations to make data-driven decisions through developing i) Reporting capabilities involving development of automated and self-service reports/dashboards; ii) Analytics Capabilities involving building of tools, science-based artefacts and defect attribution/bridges. Specifically, the role is intended to i) Own business critical (xBR, Peak, Contingency) reporting; ii) Be a partner in enabling launch of new programs/features/products by creating right metrics that showcase its adoption and success; iii) Build capabilities that benchmark and improve operations performance (e.g. Scorecards); iv) Build critical BI technology/tools aiding metric optimization, simulations, real-time tracking and alerting; v) Build defect attribution bridges for key operations metrics to enable goal setting and tracking; vi) Build datasets on Operations performance. Another critical objective of this role is to drive BI Artefacts and Practice Standardization for ROW. This will involve i) Understand landscape and define the BI Standards; ii) Drive ROW-level Standardization of Key Artefacts; iii) Influence and execute on this agenda by partnering with other teams; iv) Set BI Engineering standards and practice for us to scale support non-linearly.
A day in the life
Driving parity across marketplaces, standardization and centralized development of capabilities are the guiding principles to scale the support non-linearly to newer scope and marketplaces. The incumbent is staffed with a team of ~20 members spread across India (Blr, Hyd), Dubai and Mexico locations. The incumbent needs to decide right mix of job roles for the scope (currently has 4 job roles – BA, BI Engineer, BI Manager, BA Manager) and right locations considering HC costs and outcomes. The incumbent should actively provide career choice opportunities in line with the emerging aspirations of team members to have diverse exposure to analytics technologies and laterally move members to roles with high technical complexity (e.g., BA -> BIE). The incumbent needs to keep the team motivated, build and retain expertise, codify processes for new employees to scale up and attract talent into the team. Given wide number of stakeholders, incumbent should balance their needs while maintaining work-life harmony of team members, should drive a culture of celebrating wins (small and large) and recognition, and foster strong collaboration across members [LP: Hire and Develop, Strive to be earth’s best employer].
About the team
AOP (Analytics Operations and Programs) team powers the Data and Insights for ROW Operations. Team is responsible for development of business-critical BI and analytics solutions for Operations transcending all miles. The proposed role within AOP is a critical one requiring to lead FC Analytics for Row Geographies- 7+ years of business intelligence and analytics experience
- 5+ years of delivering results managing a business intelligence or analytics team, including employee development and performance management experience
- Experience with SQL
- Experience with ETL
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with R, Python, Weka, SAS, Matlab or other statistical/machine learning software
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