Associate Data Engineer

Are You Ready to Make It Happen at Mondelez 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:

• 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.
• Experience using data analysis to make recommendations to analytic leaders.
• Understanding of best-in-class analytics practices.
• Knowledge of indicators (KPIs) and scorecards.
• Knowledge of BI tools like Tableau, Excel, Alteryx, R, Python, etc. is a plus.

Experience:

• Data Engineering: 2 to 4 years with good expertise in the required tech stack.
• Notice Period: 0-3 months allowed.

Role & Responsibilities:

• Design and build scalable, secure, and cost-effective cloud-based data solutions.
• Develop and maintain data pipelines to extract, transform, and load data into data warehouses or data lakes.
• Implement data quality and validation processes to ensure data accuracy and integrity.
• Ensure efficient data processing of pipelines and data storage for optimal retrieval performance.
• Be an integral part of the delivery team by exhibiting close coordination within the assigned team to ensure timely completion of deliverables while fulfilling all data engineering guidelines and standards.

Technical Requirements:

• Programming: Python, PySpark, SQL
• ETL & Integration: Databricks (LDP, Lakeflow Connect, Notebooks), AecorSoft/DataSphere
• Data Warehousing: SCD Types, Fact & Dimensions, Star Schema, ETL
• Data Modeling: Dimensional modelling understanding, Erwin tool (Optional)
• Medallion Architecture: Bronze, Silver, Gold (Databricks Medallion Architecture)
• AWS Cloud Services: S3, Lambda, EC2, Glue, Redshift
• GenAI exposure: Genie/Kiro/Gemini etc.
• Scheduling Technologies: Lakeflow Jobs, Airflow
• Visualization (Optional): Power BI (Optional), GenBI (Optional)

Soft Skills:

• Problem-solving: The ability to identify and solve complex data-related challenges.
• Communication: Effective communication skills to collaborate with Engineering Leads, Data Product Managers, Data Modeler, Data Analysts, and stakeholders.
• Analytical thinking: The capacity to analyze data and draw meaningful insights.
• Attention to detail: Meticulousness in data preparation and pipeline development.
• Adaptability: The ability to stay updated with emerging technologies and trends in the data engineering field.