About the Role
The Media Data Platform (MDP) team is looking for a Data & AI Engineer to join a growing squad building MDLZ's in-house media ecosystem. You will design and deliver data pipelines, integrations, and AI-driven solutions that ingest, transform, and activate data from external media platforms — enabling smarter media investment decisions at global scale. The role sits at the intersection of data engineering, cloud infrastructure, and applied AI, working closely with Product, Engineering, and Architecture teams across Marketing & I&A Products.
Responsibilities
•Design, build, and optimise data pipelines that ingest, transform, and serve media data from external platforms (DSPs, SSPs, ad servers, social media, measurement partners) using Python, Databricks, GCP (BigQuery, Dataflow), and AWS.
•Develop and deploy AI/ML models and LLM-based workflows — including RAG pipelines, prompt engineering, and agent orchestration — to generate media performance insights and activation intelligence.
•Build and maintain APIs and backend services that surface media data and AI-generated insights to business users and downstream applications.
•Implement monitoring, logging, and alerting for pipelines and models to ensure reliability, performance, and cost-efficiency in production.
•Collaborate with Product Owners, Engineering, and Architects across Marketing & I&A Products to translate business requirements into scalable, reliable technical solutions.
•Contribute to platform architecture decisions including infrastructure-as-code (Terraform), containerisation (Docker), and CI/CD practices.
•Write clean, tested, well-documented code; participate in code reviews, sprint ceremonies, and knowledge-sharing across the team.
Requirements
•Strong proficiency in Python and experience building production-grade data pipelines or applications.
•Hands-on experience with GCP (BigQuery, DBT, Cloud Storage) and AWS, plus Databricks (Spark, Delta Lake, Unity Catalog).
•Solid understanding of data engineering — ETL/ELT, SQL, data modelling, and working with structured and unstructured data at scale.
•Exposure to AI/ML engineering — LLM integration, prompt engineering, RAG pipelines, or agent-based architectures.
•Experience with data integration patterns: REST APIs, streaming, orchestration (Airflow/Cloud Composer), and DBT.
•Solid software engineering fundamentals: Git, CI/CD, testing, Docker, and Terraform.
•Strong communication skills; able to work cross-functionally and translate technical topics to business stakeholders.
•Intellectual curiosity and a bias for learning in a fast-moving data and AI environment.
Nice to Have
•Experience in media data engineering and adtech integrations (DSPs, SSPs, ad servers, social platforms)
•Experience with agent orchestration frameworks (e.g. LangChain, LangGraph, AutoGen, CrewAI)
•Familiarity with the Databricks ecosystem (MLflow, Feature Store, Genie)
•Exposure to NLP or multimodal AI
•Experience with monitoring/observability tools (e.g. Prometheus, Grafana, CloudWatch)
•Prior experience in a CPG/FMCG environment
Conditions
•No relocation support available
