Data Engineer (Digital Intelligence)

As a Data Engineer within the Digital Intelligence unit, you will serve as a key technical representative in leveraging data and technology to drive organizational growth and enable high-velocity decision-making. You will apply your engineering expertise to design, build, and maintain a robust Marketing Data Warehouse that serves as the primary engine for Digital Customer Experience, Integrated Marketing, and Commercial Excellence initiatives.

Key Responsibilities

Data Architecture & Warehouse Management

• Infrastructure Design: Develop and standardize scalable data pipelines, create optimized database tables, and validate complex measures to streamline business efficiency.
• System Stewardship: Provide day-to-day maintenance and system support for the global Marketing Data Warehouse to ensure high performance and data availability.
• Governance & Security: Oversee warehouse administration and API development while ensuring 100% compliance with corporate IT Data Security standards and protocols.

ETL Operations & Project Delivery

• End-to-End Execution: Lead the full project lifecycle—including planning, design, execution, testing, and deployment—for critical data management and development projects.
• Quality Engineering: Implement robust ETL (Extract, Transform, Load) processes and unified data standards to ensure consistent, high-quality technical solutions across the enterprise.
• Process Optimization: Identify opportunities to automate manual workflows and improve the overall efficiency of data ingestion and processing.

Cross-Functional Collaboration

• Requirement Translation: Partner with multi-functional business teams to translate complex commercial requirements into scalable technical data solutions.
• Analytics Partnership: Collaborate closely with Data Science and Analytics teams to define project scopes and provide the clean, modeled data required for advanced modeling.
• Technical Advocacy: Effectively communicate complex data insights, architectural logic, and technical recommendations to non-technical stakeholders.

Requirements

Experience & Education

• Professional Tenure: At least 3 years of direct experience in IT Data Engineering or a closely related field.
• Cloud Data Platforms: Proficiency with modern enterprise data platforms such as Snowflake, Microsoft Fabric, or Databricks.
• Technical Stack: Strong expertise in relational database modeling and languages, specifically SQL and Python.

Core Competencies

• Communication: Exceptional ability to explain complex data structures and technical "black boxes" to business leadership and stakeholders.
• Learning Agility: A proactive self-starter with a high aptitude for learning and a willingness to continuously upskill in emerging technologies.
• Interpersonal Savvy: Ability to collaborate effectively with diverse teams across all organizational levels in a global, multicultural environment.

Preferred Qualifications

• Digital Analytics: Familiarity with Google BigQuery or similar cloud-based digital analytics tools is a significant advantage.
• Software Proficiency: Advanced knowledge of the Microsoft Office 365 suite for documentation and reporting.