Overview

　　Are you interested in a career in Data & Analytics in PepsiCo?

　　Then now is the time to explore the opportunities of PepsiCo: join us as the next Technical Data Product Associate Manager - Enterprise Data Foundation

　　Working with inspiring and experienced colleagues, you'll find that the atmosphere in our office in Barcelona is informal and engaging. With an active, get-things-done culture, this is a place where your dynamism and agility will make a difference.

　　Your mission:

　　With data deeply embedded in our DNA, PepsiCo Data & Analytics (D+A) transforms data into consumer delight. We build and organize business-ready data that allows PepsiCo's leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise.

　　At the core of our digitalization agenda is the Enterprise Data Foundation and we are looking for talented individuals to join our team to drive the roadmap and hydration of this global platform.

　　Position Overview:

　　The Technical Data Product Management Manager delivers three critical dimensions:

　　• End-to-end Data + Analytics delivery execution ownership and optimization of resource assignment, sequencing, allocation

　　• Oversee the design, building, and managing of how business-ready data lives within the Enterprise Data Foundation

　　• Drive coordination between Domains, Products and Projects to help drive re-use and reduce redundancy/duplication in the D+A and broader PEP portfolio

　　Responsibilities

　　Your day to day with us:

　　• Build data roadmaps to meet hydration targets and support timely delivery for global data initiatives and programs

　　• Manage delivery against key technical delivery milestones

　　• Collaboratively manage reporting that measures D+A's execution against key hydration milestones

　　• Lead x-chapter Pod resources towards shared goal

　　• Participate in PI Planning to coordinate between domain goals and program/product milestones

　　Technical Documentation and Artifacts

　　• Work with multiple stakeholders to author Epic and feature definitions and guide user stories to drive delivery

　　• Collaboratively design and document the data product artifacts required by the team.

　　• Ensure technical documentation is readily available for data products produced and that value is communicated to key stakeholders

　　• Ensure cataloguing of data in the Data Foundation is accessible to business stakeholders

　　Manage Stakeholders

　　• Help track benefits of these insights to leadership to drive acceptance

　　• Work with program Technical Data Product Managers to ensure expectations from D+A are managed and seek to eliminate disconnect on what can be addressed

　　Qualifications

　　What will you need to succeed?

　　• Bachelor's degree

　　• 6+ years of Product Management, Data Analytics, Data Science or Data Management and Operations in business facing functions

　　• 1+ years of experience in Food/Beverage/Manufacturing or Consumer Products industry

　　• 2+ years of experience leading/building advanced analytics and big data solutions, large scale data modeling, or building enterprise SaaS

　　• Experience working in a data-centric business environment

　　• Excellent ability to identify sources of value from data analytics across core business domains (such as marketing, risk, and operations) and to define paths realizing that value

　　• Familiarity with principles and tools for data governance and stewardship

　　• History of working in agile environments and successful estimation and delivery of complex products

　　• Effective communication skills and an ability to break down complex information into relevant and digestible points for both inside D+A and externally to the business

　　• Excellent leadership skills, with a team-player attitude to drive the end-to-end implementation of use cases under time pressure

　　• Demonstrated ability to drive business-oriented and innovative solutions using data science, feature engineering and machine learning