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

In this role, you will be a key technical leader in developing our cutting-edge Supply Chain Data Product ecosystem. you'll have the opportunity to design, build, and automate data ingestion, harmonization, and transformation processes, driving advanced analytics, reporting, and insights to optimize Supply Chain performance across the organization. You will play an instrumental part in engineering robust and scalable data solutions, acting as a hands-on expert for Supply Chain data, and contributing to how these data products are visualized and interacted with.

What you need to know about this position:

Our team is at the forefront of building Data Products driving AI and Analytics across Mondelez. As a Sr. Data & Analytics Engineer within the Mondelez Data Product team, you will be working towards the following objectives:

Build and Enhance Supply Chain Data Products: Design, develop, and maintain reusable Data Products as the single source of truth for Supply Chain, logistics, manufacturing, and related data domains.
End-to-End Data Pipeline Development: Leverage data from diverse internal systems (especially SAP ECC/S4HANA, SAP BW/HANA , and other Supply Chain platforms) and external data sources, ingesting it into our centralized Google Cloud Platform (GCP) data platform.
Enable Data Governance and Management: Implement and champion Data Governance and Data Management standards, including data cataloging, documentation, security protocols, robust data quality controls, master data management (MDM) principles, and data democratization.
Scalable Data Modeling & Implementation: Design, implement, and optimize reusable and scalable data models following industry best practices and high coding standards, ensuring efficient data flow for analytical consumption.
Hands-on Technical Leadership: Play a lead technical role throughout the entire Software Development Lifecycle (SDLC) - from requirements gathering and design to development, testing, deployment, and hypercare.
Product and Value-Driven Mindset: Build each feature with a Product Owner mindset, focusing on delivering business value with efficiency, agility, and a strong bias for action. This includes considering the end-user experience of the data products.
Technical Point of Contact: Act as a key technical expert and point of contact within the Data Engineering team for Supply Chain data initiatives.

What extra ingredients you will bring:

Proven hands-on experience designing and developing complex data models and high-quality, performant data pipelines.
Passion for leveraging data to drive tangible improvements in Supply Chain efficiency, cost reduction, and operational excellence.
Ability to thrive in an entrepreneurial, fast-paced setting, managing complex data challenges with a solutions-oriented approach.
Excellent communication and collaboration skills to facilitate effective teamwork, engage with Supply Chain stakeholders, and explain complex data concepts to both technical and non-technical individuals.
Strong problem-solving skills and business acumen, particularly within the Supply Chain domain.

Education / Certifications:

Bachelors degree in Information Systems/Technology, Computer Science, Analytics, Engineering, or a related field.
10+ years of hands-on experience in data engineering, data warehousing, or a similar technical role, preferably in CPG or manufacturing with a strong focus on Supply Chain data.

Job specific requirements (Hands-on Experience Focus):

SAP Data Expertise: Deep hands-on experience in extracting, transforming, and modeling data from SAP ECC/S4HANA (modules like MM, SD, PP, QM, FI/CO) and SAP BW/HANA. Proven ability to understand SAP data structures and business processes within Supply Chain.
Data Pipeline Development: Design, build, and maintain robust and efficient ETL/ELT processes for data integration, ensuring data accuracy, integrity, and timeliness.
Cloud Data Engineering (GCP Focused):
Strong proficiency and hands-on experience in data warehousing solutions and data engineering services within the Google Cloud Platform (GCP) ecosystem (eg, BigQuery, Dataflow, Dataproc, Cloud Composer, Pub/Sub).
Hands-on experience with Databricks (ideally deployed on GCP or with GCP integration) for large-scale data processing, Spark-based transformations, and advanced analytics is highly desirable.
Data Modeling & Warehousing: Hands-on experience in developing efficient data models (eg, dimensional, Data Vault) and building/maintaining data warehouses, primarily on GCP.
Programming & Automation: Strong proficiency in SQL and Python for data manipulation, pipeline development, and automation.
API Integration: Experience working with multiple APIs to extract and/or send data for system integration.
BI & Analytics Enablement: Collaborate with data scientists, analysts, and business users to provide high-quality, reliable data for their analyses and models. Support the development of data consumption layers, including dashboards (eg, Tableau, Power BI).
UI/Front-End Experience (Desirable): Experience with front-end technologies and JavaScript frameworks (eg, React JS, Angular, Vue.js ) for building custom data visualizations, interactive dashboards, or user interfaces for Data Products is a significant plus.
System Monitoring & Optimization: Monitor data processing systems and pipelines to ensure efficiency, reliability, performance, and uptime; proactively identify and resolve bottlenecks.
DevOps & CI/CD: Prior hands-on work in a DevOps environment or strong understanding and experience implementing CI/CD pipelines for data solutions
Data Governance & Security: Ensure compliance with data privacy regulations and implement data security standards and best practices within data solutions on GCP.
Industry Knowledge: Solid understanding of the consumer goods industry, particularly Supply Chain processes and relevant key performance indicators (KPIs).
Agile Methodologies: Experience working in Agile development environments.
Continuous Learning: Stay updated on the latest technologies and best practices in data engineering, GCP data services, Databricks, UI development trends, and database management.

Supply Chain Data Product Manager