Senior Data Technology Engineer

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

More about this role
What you need to know about this position:
The Senior Data Technology Engineer will be a key member of the Data COE within the Technology Enablement team, responsible for designing, building, and optimizing enterprise-scale data onboarding platforms. This role demands deep expertise in Google Cloud Platform (GCP), strong Python development skills, and hands-on experience with modern data engineering tools such as Apache Airflow, GCP Composer, GKE, and DataProc.
The engineer will collaborate with cross-functional teams to deliver robust data ingestion, transformation, and orchestration frameworks, ensuring observability, governance, and automation across the enterprise data ecosystem.
What extra ingredients you will bring:

• Design, develop, and optimize scalable data onboarding solutions.
• Implement reusable components for data ingestion, transformation, and orchestration.
• Build backend services and APIs in Python to support data engineering workflows.
• Collaborate with platform engineers and product owners to gather requirements and deliver fit-for-purpose solutions.
• Integrate observability, monitoring, and logging into data pipelines for reliability and transparency.
• Ensure adherence to best practices in software engineering, DevOps, and cloud-native development.
• Contribute to CI/CD pipeline setup, automated testing, and deployment strategies for data engineering projects.
• Mentor junior engineers and promote knowledge sharing across teams.

Education / Certifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related discipline.
• 6+ years of data engineering experience, with at least 5 years working with Python, PySpark, GCP BigQuery, and Databricks.
• Backend development experience in Python with exposure to microservices architecture.
• Experience 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.

Job specific requirements:

• Proven experience with building internal development platforms.
• Familiarity with data quality rules and tools (e.g., Ataccama) and observability tools (e.g., Datadog).
• Understanding of data governance and data mesh concepts.
• Strong debugging and problem-solving skills.
• Self-motivated, innovative, and eager to explore new solutions.
• Agile delivery mindset with strong emphasis on automation and reusability.
• Cloud Platform: GCP
• Data Warehousing: BigQuery
• Containerization: Docker, Kubernetes
• CI/CD & DevOps: CI/CD pipelines, testing frameworks, software observability
• Data Governance: Collibra, Ataccama, Datadog, metadata management, lineage tools, data mesh concepts
• Data Engineering Tools: DBT, Apache Airflow, Databricks

No Relocation support available
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