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Data Product Manager

Mondelez International

Toronto, CanadaMid
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Location
Toronto, Canada
Work model
On-Site
Level
Mid
Posted

Skills

AgileAirflowBigQueryGCPdbt

About this role

Job Description

Are You Ready to Make It Happen at Mondelēz International? Join our Mission to Lead the Future of Snacking. Make It With Pride. Together with analytics team leaders you will support our business with excellent data models to uncover trends that can drive long-term business results. How you will contribute You will:  Work in close partnership with the business leadership team to execute the analytics agenda Identify and incubate best-in-class external partners to drive delivery on strategic projects Develop custom models/algorithms to uncover signals/patterns and trends to drive long-term business performance Execute the business analytics program agenda using a methodical approach that conveys to stakeholders what business analytics will deliver What you will bring A desire to drive your future and accelerate your career and the following experience and knowledge: Using data analysis to make recommendations to senior leaders Technical experience in roles in best-in-class analytics practices Experience deploying new analytical approaches in a complex and highly matrixed organization Savvy in usage of the analytics techniques to create business impacts More about this role IN THIS ROLE you will lead end-to-end lifecycle ownership of one or more Data Products on Mondelēz's DBx-based enterprise data platform. Accountable for requirements, roadmap, design governance, delivery quality, operational oversight, and retirement execution. Combines strategic product ownership with hands-on analytical execution. WHAT THIS TEAM AND ROLE DOES : Part of the MDS Data Products team driving AI-native transformation. Global data product strategy, delivery, and governance across the full lifecycle (demand intake → discovery → design → build → test → deploy → operate → retire). Works at the intersection of business strategy, data governance, and technical delivery. WHAT YOU WILL BRING : Experience bridging business and technical teams in data product delivery; passion for data governance and quality; strong stakeholder management across various stakeholders. JOB REQUIREMENTS : Own and maintain the data product roadmap Facilitate data contracts with stakeholders Engage with cross functional teams on demand prioritization and enterprise-level decisions Lead requirements gathering, functional analysis, fit-gap assessments and scoping Oversee design fitment, source discovery, and Source-to-Target Mapping (STTM) Approve initial designs, HLD, DIF documentation Execute STTM mapping and document gap findings Approve DQ validation, KPI acceptance, and release documentation Prepare test cases, execute test scripts, and produce test evidence documentation Coordinate UAT logistics - liaise with business stakeholders and document outcomes Own UAT sign-off and Data Product assessments Drive and own the Data Product Certification process Prepare certification artefacts and supporting documentation Own data product release and CAB documentation SPECIFIC SKILLS AND EXPERIENCES: Bachelor’s degree in computer science, Information Systems, Business Analytics, or related field 5+ years in data product management, data analytics, or data engineering roles Strong understanding of data lifecycle management (ingestion through retirement) Experience with cloud data platforms (GCP/BigQuery preferred) Hands-on experience with STTM mapping, DQ validation, and data quality frameworks Familiarity with data governance frameworks, metadata management, and data contracts Experience working in Agile/Scrum delivery environments Strong stakeholder management and communication skills GOOD TO HAVE SKILLS AND EXPERIENCES: Experience with DBT, Airflow/Composer, or similar data pipeline tools Knowledge of FAIR data principles and data certification frameworks Background in FMCG/CPG industry data environments Familiarity with data catalog tools (Collibra, Alation, Dataplex) Understanding of canonical data modeling and enterprise data standards

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