Responsibilities

• Team Building and Technical Leadership: Recruit, develop, and mentor a team of Data Quality Analysts into technical specialists who understand both the "how" and the "why" behind data operations. Create development pathways that build expertise in data engineering, governance frameworks, and business context, preparing team members for expanded technical or leadership roles.
• Scalable Infrastructure and Process Design: Design and implement data operations workflows, quality control frameworks, and monitoring systems that can scale across multiple portfolios and business functions. Build automation and efficiency into routine processes to increase team capacity without proportional headcount growth.
• Standards Implementation and Governance Execution: Lead the effective implementation of data governance and architectural standards defined by the Commercial Data Governance team. Ensure all portfolio-level data remains compliant with enterprise naming conventions, taxonomies, and quality thresholds, while adapting global standards to marketing-specific use cases.
• Technical Integrity and Quality Control: Establish and oversee routine data hygiene audits, validation workflows, and quality monitoring dashboards. Implement proactive alerting systems that identify data anomalies before they impact downstream analytics or executive reporting.
• Data Pipeline Management and System Health: Monitor the health and performance of data pipelines between the central data warehouse and marketing execution platforms (e.g., Eloqua, Salesforce, IIRIS, CDP). Troubleshoot synchronization issues, latency problems, and integration failures with speed and precision.
• Strategic Partnership and Opportunity Identification: Work closely with the Data Strategy Manager to identify opportunities where improved data quality, new data sources, or enhanced technical capabilities can unlock business value. Translate strategic analytics initiatives into operational requirements, ensuring the technical foundation supports innovation and advanced analytical use cases.
• Governance Liaison and Strategic Representation: Serve as the technical point of contact for the central Governance team, ensuring marketing-specific requirements are represented in global standards. Advocate for the CoE's operational needs, flag emerging data quality risks, and contribute to enterprise-wide data strategy discussions.
• Operational Oversight and Source Data Management: Maintain local control over data entry points and system integrations to ensure data accuracy at the source. Implement validation rules, data entry standards, and user training to prevent quality issues from entering the ecosystem, particularly during centralized transitions or system migrations.
• Cross-Functional Collaboration and Technical Consultation: Collaborate with the Senior Analytics Manager to ensure data operations support analytical delivery needs and reporting excellence. Provide technical guidance to portfolio teams on data best practices, system capabilities, and optimization opportunities.
• Continuous Improvement and Innovation: Identify opportunities to leverage automation, AI-driven data quality tools, and emerging technologies to enhance operational efficiency. Stay current with industry best practices in data operations and governance, bringing external innovations into the organization.

What Success Looks Like

• Technical Team Excellence: Development of a high-performing data operations team characterized by deep technical expertise, proactive problem-solving, low error rates, and successful advancement of team members into expanded technical or leadership roles.
• Scalable and Resilient Operations: Establishment of automated monitoring, quality control frameworks, and operational processes that successfully support multiple business functions with minimal manual intervention and clear pathways for scaling as data volumes and complexity grow.
• Standard Compliance and Governance Leadership: 100% adherence to Commercial Data Governance standards across all managed portfolios, with the CoE recognized as a model for effective governance implementation across the enterprise.
• Data Trust and Analytical Velocity: A validated, reliable data foundation that allows the Marketing Analytics team to deliver insights without manual data cleaning or intervention, reducing time-to-insight and increasing stakeholder confidence in analytical outputs.
• Proactive System Health: Proactive identification and resolution of data source errors, pipeline issues, and quality anomalies before they impact executive reporting or business decisions, with measurable reductions in data-related incidents and escalations.
• Operational Efficiency Gains: Documented improvements in data processing speed, quality control automation, and team productivity through process optimization and technical innovation.