The Data Quality Engineer will ensure the accuracy, consistency, and reliability of data across Rentokil Initial’s digital marketing platforms. This role will focus on building and implementing data quality frameworks that governance processes within the Google Cloud Platform (GCP) to support analytics and insights.
As part of the Digital Analytics team, the Data Quality Engineer will develop validation processes, troubleshoot data issues, and maintain compliance with global data regulations. By enabling high-quality data, this role will directly support analytics-driven decision-making and improve campaign performance globally

Key Responsibilities

• Data Quality Framework and Validation:
• Design and implement scalable data quality frameworks to ensure data accuracy, consistency, and completeness across digital marketing platforms.
• Develop automated validation processes, including integrity checks and error detection, for platforms such as GA4, CRM systems, and social media tools.
• Monitoring and Issue Resolution
• Establish monitoring tools to track data health and quality, proactively identifying and resolving inconsistencies or errors.
• Collaborate with data providers, the Data Architect, and analytics teams to address and prevent recurring data issues.
• Data Governance and Compliance:
• Partner with the Data Architect to establish and enforce data governance policies that align with GDPR and other global regulations.
• Document and maintain standards for data validation and quality management to ensure consistent application across regions.
• Collaboration and Stakeholder Engagement:
• Work closely with the Digital Analytics & Insights Manager and other stakeholders to align data quality processes with analytics and reporting needs.
• Communicate data quality metrics and improvement plans to technical and non-technical stakeholders.
• Continuous Improvement:
• Stay updated on best practices in data quality management and incorporate new tools and techniques to enhance processes.

REQUIREMENTS

• Experience:
• 3-5 years of experience in data quality engineering, data governance, or a similar role, ideally within a digital marketing or analytics environment.
• Technical Skills:
• Data Validation and Quality Tools: Proficient in validation and monitoring tools; experience with automated testing frameworks is a plus.
• Google Cloud Platform (GCP) Expertise: Strong knowledge of GCP tools, including BigQuery and Dataflow, to support data quality processes.
• SQL Expertise: Skilled in querying and validating data from various sources, particularly in data warehousing solutions like BigQuery.
• Familiarity with Digital Marketing Platforms: Knowledge of GA4, CRM systems, and social media platforms, with experience ensuring data quality across these channels.
• Reporting: Experience with data visualisation platforms (e.g., Google Data Studio, Tableau).
• Compliance and Governance:
• Familiarity with GDPR and global data handling regulations, with experience implementing compliant data quality processes.

• Data Quality Framework and Validation:
• Design and implement scalable data quality frameworks to ensure data accuracy, consistency, and completeness across digital marketing platforms.
• Develop automated validation processes, including integrity checks and error detection, for platforms such as GA4, CRM systems, and social media tools.
• Monitoring and Issue Resolution
• Establish monitoring tools to track data health and quality, proactively identifying and resolving inconsistencies or errors.
• Collaborate with data providers, the Data Architect, and analytics teams to address and prevent recurring data issues.
• Data Governance and Compliance:
• Partner with the Data Architect to establish and enforce data governance policies that align with GDPR and other global regulations.
• Document and maintain standards for data validation and quality management to ensure consistent application across regions.
• Collaboration and Stakeholder Engagement:
• Work closely with the Digital Analytics & Insights Manager and other stakeholders to align data quality processes with analytics and reporting needs.
• Communicate data quality metrics and improvement plans to technical and non-technical stakeholders.
• Continuous Improvement:
• Stay updated on best practices in data quality management and incorporate new tools and techniques to enhance processes