Technical Product Lead

Job Description 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 youwill support our businesswith 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 drivelong-term business performance - Execute the business analytics program agenda using a methodical approach that conveysto 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 matrixedorganization - Savvy in usage of the analytics techniques to create business impacts About the Role The Data COE Technical Product Owner will be part of Technology Enablement team and would be responsible for defining and implementing best practices, frameworks, and tools that support scalable data ingestion and engineering processes. This includes building robust backend services and intuitive front-end interfaces to enable self-service, observability, and governance in data pipelines across the enterprise. Key Responsibilities: - Lead the development of reusable software components, libraries, and frameworks for data ingestion, transformation, and orchestration. - Design and implement intuitive user interfaces (dashboards, developer portals, workflow managers) using React.js and modern frontend technologies. - Develop backend APIs and services to support data engineering tools and platforms. - Define and enforce software engineering standards and practices across the Data COE for developing and maintaining data product. - Collaborate closely with data engineers, platform engineers, and other COE leads to gather requirements and build fit-for-purpose engineering tools. - Integrate observability and monitoring features into data pipeline tooling. - Lead evaluations and implementations of tools to support continuous integration, testing, deployment, and performance monitoring. - Mentor and support engineering teams in using the frameworks and tools developed. Qualifications: - Bachelor's or master's degree in computer science, Engineering, or related discipline. - 10+ years of data engineering experience, with at least 5 years in software engineering, platform, or infrastructure roles. - 3+ years of cloud experience on Google Cloud Platform. - Backend development experience in Python with exposure to microservices architecture FAST APIs and RESTful APIs. - Experience working with data engineering tools such as Apache Airflow, Kafka, Spark, Delta Lake, and DBT. - Familiarity with GCP cloud platforms, containerization (Docker, Kubernetes), and DevOps practices. - Strong understanding of CI/CD pipelines, testing frameworks, and software observability. - Ability to work cross-functionally and influence without direct authority. Preferred Skills: - Proven experience with building internal development platforms or self-service portals. - Familiarity with data catalogue, metadata, and lineage tools (e.g., Collibra). - Familiarity with Data Quality rules and tools like Ataccama, Data observability tools like Datadog. - Understanding of data governance and data mesh concepts. - Agile delivery mindset with strong emphasis on automation and reusability. Tools and Technologies: - Frontend Development: React.js - Backend Development: Python, FAST APIs, RESTful APIs, Microservices Architecture - Cloud Platform: GCP - Data warehousing tech: Big Query - Containerization: Docker, Kubernetes - CI/CD & DevOps: CI/CD pipelines, Testing Frameworks, Software Observability - Data Governance: Data catalogue tools (e.g., Collibra), Metadata Management, Lineage Tools (Collibra), Data Quality tools (Ataccama), Data Observability tools (Datadog), Data Mesh concepts. - Data Engineering Tools: DBT, Apache Airflow, databricks. Why Join Us - Play a strategic role in developing the data engineering backbone for a next-generation enterprise Data COE. - Work with cutting-edge data and software technologies in a highly collaborative and innovative environment. - Drive meaningful change and enable data-driven decision-making across the business. Within Country Relocation support available and for candidates voluntarily moving internationally some minimal support is offered through our Volunteer International Transfer Policy Business Unit Summary At Mondelēz International, our purpose is to empower people to snack right by offering the right snack, for the right moment, made the right way. That means delivering a broad range of delicious, high-quality snacks that nourish life's moments, made with sustainable ingredients and packaging that consumers can feel good about. We have a rich portfolio of strong brands globally and locally including many household names such as , and biscuits , and chocolate candy and gum. We are proud to hold the top position globally in biscuits, chocolate and candy and the second top position in gum. Our 80,000 makers and bakers are located in more than80countries and we sell our products in over150countries around the world. Our people are energized for growth and critical to us living our purpose and values. We are a diverse community that can make things happen-and happen fast. Mondelēz International is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation or preference, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. Job Type Regular Analytics & Modelling Analytics & Data Science