• Assist in developing and maintaining data pipelines and ETL/ELT processes under the guidance of more senior engineers. • Write Python and SQL to extract, transform, validate, and load data from common sources. • Perform data quality checks (validation, reconciliation, basic monitoring) and help troubleshoot data issues. • Develop dashboards and analytic products using data visualization tools (e.g., Power BI, Tableau). • Support cloud-based data workloads (e.g., Azure/AWS/GCP basics) and learn platform-native services and patterns. • Document pipeline steps and technical processes to support maintainability and knowledge transfer. • Participate in team delivery rhythms (standups, sprint ceremonies) and contribute to reviews with a learning mindset. • Design, build, test, and maintain scalable data pipelines (batch and/or streaming as applicable) with increasing independence. • Integrate data from multiple sources, resolve inconsistencies, and deliver curated datasets for analytics and operational use. • Own data quality for assigned domains by implementing validation checks, reconciliation, and monitoring/alerting patterns. • Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, Azure, GCP). • Optimize performance of pipelines and queries (tuning, partitioning patterns, efficient compute usage). • Collaborate cross-functionally with analysts, data scientists, and stakeholders to translate requirements into technical designs and delivery plans. • Produce and maintain technical documentation for data flows, data models, and operational procedures. • Contribute to governance and compliance practices (access controls, lineage awareness, controlled data handling) within your scope. • Lead the design and build of scalable data pipeline architectures and tools, including patterns for reliability, security, and maintainability. • Drive ETL/ELT and data quality strategy (frameworks, standards, repeatable testing/monitoring approaches) and raise engineering maturity across the team. • Architect solutions in cloud data platforms (e.g., Azure + Databricks, Snowflake) and guide implementation tradeoffs (cost, performance, scalability, governance). • Design data stores and interactions across storage types (relational, warehouse, lake/lakehouse, and NoSQL where needed) aligned to use cases. • Enable data science / ML readiness by delivering well-modeled, reliable, well-documented datasets and features. • Lead requirements gathering and technical planning; translate ambiguous problem statements into actionable architectures, backlogs, and delivery increments. • Champion data quality and governance standards through the development of sophisticated data quality frameworks, dashboards, and feedback loops to ensure transparency in data completeness, consistency, and quality for partners and researchers. • Own client and stakeholder engagement for your workstream, including organizing/leading meetings, producing clear written outputs, and tracking follow-through. • Mentor and review: provide strong code/design reviews, coach engineers, and help remove technical blockers.

• Lead, mentor, and grow a team of data engineers and architects • Define and execute the technical roadmap for production database systems (MySQL, PostgreSQL, DynamoDB, Elastic) • Own the architecture and governance of binlog replication, logical replication, and CDC workflows • Drive strategy and reliability for ELT/ETL pipelines and Kafka-based streaming architectures • Set standards for performance optimization, query tuning, indexing, and database scaling across teams • Oversee backup, failover, disaster recovery (PITR), and incident response for all production data systems • Drive cost efficiency, infrastructure optimization, and monitoring across cloud-managed data services (AWS RDS, Aurora, DynamoDB) • Champion data integrity, security, and compliance standards across all data engineering work • Partner cross-functionally with backend, data science, infrastructure, and product teams to align on data platform priorities • Establish engineering guardrails, best practices, and documentation to enable team autonomy and quality at scale • Lead the evaluation and selection of next-generation data warehousing technology (Snowflake, Databricks, AWS Redshift Serverless) — assessing performance, cost, ecosystem fit, and migration complexity to inform a platform decision • Own the design of an upgraded data model for the warehouse in partnership with data engineers and architects, establishing standards for schema design, partitioning, access patterns, and downstream consumption • Oversee the end-to-end migration from the current Redshift warehouse — planning the phased approach, managing cutover risk, and ensuring continuity of downstream reporting and analytics throughout

• Assist in building and maintaining ETL/data pipelines using Python and PySpark • Ingest, transform, and validate data from multiple sources • Support data modeling and schema design for structured datasets • Use Git for version control and collaborate with engineering teams • Perform unit testing, code reviews, and performance optimization • Contribute to technical documentation of data workflows and pipelines • Support feature testing and controlled releases in QA/dev environments • Perform exploratory analysis using Jupyter/Amazon SageMaker notebooks • Work in a Scrum/Agile environment with clear communication and collaboration

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: - Execute the business analytics agenda in conjunction with analytics team leaders - Work with best-in-class external partners who leverage analytics tools and processes - Use models/algorithms to uncover signals/patterns and trends to drive long-term business performance - Execute the business analytics 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 analytic leaders - Understanding in best-in-class analytics practices - Knowledge of Indicators (KPI's) and scorecards - Knowledge of BI tools like Tableau, Excel, Alteryx, R, Python, etc. is a plus Purpose of Role The Visualization & Semantic engineer will be an expert in designing, implementing, and optimizing the Semantic Layer for business self-service consumption as well as project implementation. A successful candidate will be able to provide deep expertise and capacity to share their knowledge through training and awareness sessions to multiple audiences. Main Responsibilities - You will work closely with all Mondelez D&A projects to guide, control, and build efficient Data Solutions that are Self-Service ready: - Create / design templates and best practices related to Semantic, data modelling, & Visualization. - Train the trainer: You will be the lead Power Bi & Semantic trainer. - Consult & help project execution as the key point for expertise. - Keep current & test innovation & new functionalities to validate them for production usage. - Participate in the semantic & visualization Community of Practice success. Career Experiences Required & Role Implications - Bachelor's degree, Master's in IT related field preferred - 5+ years' experience in Consulting or IT, leading the implementation of data solutions - 3-5 years of experience around the Semantic Layer, Data Models, and KPI calculation. - Demonstrate prior experience leading complex data design with multi-dimensional models and custom aggregations. - 2-3 years Power BI / Dax experience, GCP/Big Query data sources is a plus. - Understanding of data structures and algorithms with strong problem-solving skills. - Experience solving Data Analysis Challenges, such as Performance, References, Quality, integration, GCP, and/or Azure data solutions certification is a plus. - Understanding of Machine Learning and statistical forecasting activity is a plus. 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 Oreo, belVita and LU biscuits; Cadbury Dairy Milk, Milka and Toblerone chocolate; Sour Patch Kids candy and Trident 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 than 80 countries and we sell our products in over 150 countries 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