Data Engineer

Ogilvy, part of WPP, has been creating impact for brands through iconic, culture-changing, value-driving ideas since the company was founded by David Ogilvy 75 years ago. It builds on that rich legacy through Borderless Creativity – innovating at the intersections of its advertising, public relations, relationship design, consulting, and health capabilities with experts collaborating seamlessly across over 120 offices in nearly 90 countries. Ogilvy currently ranks as the #1 global agency network for creative excellence and effectiveness by WARC, signifying its ability to deliver creative solutions that drive unreasonable impact for clients and communities. Ogilvy is a WPP company (NYSE: WPP). For more information, visit Ogilvy.com, and follow us on LinkedIn, X, Instagram, and Facebook.

WPP is the trusted growth partner for the world’s leading brands. We unite cutting-edge media intelligence and data solutions, world-class creativity, next-generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth. For more information, visit WPP.com.

We are looking for someone to join our Data department as a Data Engineer, helping us manage the integration and consolidation processes of our clients' data. In both on-premise and cloud environments, you will be responsible for designing the required information system architecture and managing and maintaining our clients' databases.

We’re looking for a proactive and analytical person who can adapt to a multidisciplinary team and is eager to help us tackle the many challenges our clients face.

• University degree in Computer Science, Software Engineering, or related fields with a technological specialization in data analysis and engineering.
• Plus: Master's or postgraduate degree in Big Data, Machine Learning, Cloud Computing, or Data Engineering.
  
  

• Data Architecture & Systems: Extensive experience in designing, implementing, and optimizing complex, distributed information system architectures, including relational and NoSQL databases.
• Cloud Environments: Deep expertise in defining, implementing, managing, and maintaining enterprise-scale Cloud environments. Expert command of Azure and Google Cloud Platform (GCP), including PaaS/SaaS, serverless computing (Functions, Cloud Run, Azure Functions), containers (Docker, Kubernetes/AKS/GKE), and native cloud data services.
• Modern Data Warehouses: Advanced experience with cloud-native Data Warehouses like Snowflake, including dimensional data modeling, query optimization, and cost/performance management.
• Data Orchestration & Pipelines: Robust experience with data orchestration tools and building complex ETL/ELT pipelines using Pentaho, Apache Airflow, Data Factory, or similar, including CI/CD implementation for data pipelines.
• Advanced SQL: Demonstrable experience with advanced SQL, including complex analytical functions, query optimization, stored procedures, and efficient schema design.
• Software Development: Demonstrable experience with Javascript and Node.js, and Python for scripting, data automation, and ML model development.
  
  

• Customer Data Platforms (CDP): Experience in strategic definition, implementation, management, and maintenance of CDP solutions, preferably Segment, and their integration.
• Enterprise System Integration: Desirable experience with enterprise-level integration tools like Mulesoft or microservices architectures with REST/GraphQL APIs.
• Salesforce Ecosystem: Valuable experience working with Salesforce (Sales Cloud, Service Cloud, Marketing Cloud – SFMC), including data integration and automation.
  
  

• Deep knowledge and certifications in managing and optimizing cloud environments (Azure, GCP).
• Advanced knowledge and certifications in Snowflake (SnowPro Advanced/Architect is a plus).
• Proficiency in CDP tools (especially Segment).
• Knowledge of CRM / Marketing Automation tools (SFDC, SFMC, Adobe Campaign, Selligent Marketing Cloud).
• Advanced SQL knowledge (Analytical Functions, T-SQL, PL/SQL).
• Proficiency in programming languages like Javascript, Node.js, and Python.
• Strong understanding of Data Quality principles, Data Governance, and Normalization.
• Solid understanding of Machine Learning and AI algorithms for data enrichment and transformation.
• Experience with data streaming architectures (Kafka, Pub/Sub, Azure Event Hubs).
• Familiarity with MLOps and DataOps concepts.
  
  

• Ability to analyze and solve complex problems.
• Project management and organizational skills for end-to-end data projects.
• Meticulous attention to detail and commitment to data quality.
• High capacity for discernment and prioritization in dynamic environments.
• Technical leadership and mentoring of junior profiles.
• Strategic thinking and innovation-oriented mindset.
• Excellent communication and presentation skills for clients and multidisciplinary teams.
  
  

• Spanish (Native or Bilingual proficiency).
• English (High level C1/C2 - Essential for communication with international clients).
  
  

• →Lead strategic requirements gathering, high-level design, and implementation of complex data solutions for client communication initiatives.
• →Design and evolve scalable, efficient data architectures, selecting appropriate cloud-native technologies for each project.
• →Design, develop, and optimize ETL/ELT data integration processes to ensure high-quality data ingestion, transformation, and availability.
• →Implement end-to-end data solutions, ensuring validation, thorough documentation, and adherence to best practices in data engineering and security.
• →Provide training and knowledge transfer to internal teams and clients.
• →Maintain, evolve, and optimize existing data projects, ensuring monitoring, incident resolution, and continuous improvement.
• →Manage and optimize client databases, ensuring performance, integrity, and security.
• →Innovate with AI/ML: Implement data cleansing, normalization, consolidation, deduplication, and enrichment systems using ML/AI algorithms, including predictive scoring and customer clustering.
• →Ensure Data Quality and Governance: Implement DQ mechanisms, guarantee data integrity, and ensure GDPR compliance across all processes.
• →Strategically collaborate with clients: Participate in high-level meetings to assess current information system architectures, propose innovative improvements, and lead solution definitions for future challenges and new data platforms.
• →Mentor and guide other data engineers, fostering best practices and professional development.