Data Engineer

Job Description

Implement the metadata, catalog, semantic, discovery, and AI enablement capabilities that allow users and agents to find, understand, and consume enterprise data.

• Strong experience with SQL, Python, and cloud-based platforms like AWS, GCP, or Azure.
• Solid understanding of data modelling, pipelines and orchestration tools (Airflow, dbt, or similar).
• Familiarity with modern data ecosystems — e.g. Spark, Databricks, Snowflake, BigQuery or Redshift.
• Experience working with CI/CD, version control and testing best practices.
• A proactive approach to solving problems and improving systems.
• Excellent communication skills — you can translate technical concepts into language anyone can understand.
• A passion for data and how it can drive better decisions in media, marketing and beyond. 

Experience with technologies and platforms such as

• OpenMetadata, DataHub, Atlan
• Knowledge graph and semantic technologies
• Neo4j and graph databases
• Vector databases, RAG architectures, MCP frameworks
• Metadata management and data catalog platforms

Job Responsibilities

• Building metadata and catalog services
• Implementing knowledge graph and semantic capabilities
• Delivering data discovery and search experiences
• Enabling AI-ready data foundations
• Building context services for AI agents and applications
• Providing subject matter expertise on metadata and AI data services

Department/Project Description

Build the core data engineering capability that enables WPP Open applications, AI agents, analytics products, and business workflows to securely discover, access, combine, govern, and activate data across enterprise systems, client environments, media platforms, and third-party providers.

We are seeking senior engineers who have:

• Delivered enterprise-scale platform capabilities rather than traditional ETL solutions.
• Led implementation of complex data platforms in production environments.
• Worked with cloud-native technologies on Microsoft, Google Cloud, AWS, or comparable ecosystems.
• Built reusable services, APIs, integrations, and platform components.
• Supported operational applications and AI workloads, not just reporting and analytics.
• Operated within highly governed enterprise environments.
• Experience implementing data virtualization, federation, metadata platforms, governance capabilities, media data integration, or similar large-scale platform services.
• Strong opinions informed by hands-on delivery experience and a willingness to contribute to technical direction.