Data Product Lead (Dallas, TX)

Key Responsibilities

• Own technical quality, delivery velocity, and overall execution for all work within the Data POD.
• Lead hands-on reviews of solution designs, B2B data architectures, XDM schemas, configurations, integrations, and deployment plans.
• Establish and enforce quality standards, including peer reviews, QA validation processes, deployment checklists, operational runbooks, and governance controls.
• Manage and mentor offshore data engineering resources, providing clear specifications, acceptance criteria, feedback, and delivery oversight.
• Develop and maintain playbooks, templates, standards, and reusable implementation patterns for data collection, integration, and governance initiatives.
• Design and oversee Adobe Web SDK implementations, Edge Network / Data Streams configuration, tag management frameworks, and enterprise data layer architectures.
• Lead CRM, billing, and campaign data integrations into AEP, including lead-to-account matching and account-based data models.
• Define and maintain XDM schemas, datasets, ingestion strategies, and B2B identity frameworks—accounts, contacts/leads, opportunities, and buying groups—that support enterprise use cases.
• Ensure data quality, governance, lineage, and compliance requirements are incorporated into all solutions.
• Represent the Data POD across governance forums, including sprint reviews, PI planning sessions, steering committees, and executive leadership reviews.
• Translate business priorities and Product Manager requirements into executable sprint plans and delivery roadmaps.
• Manage POD intake, prioritization, capacity planning, and dependency tracking.
• Communicate delivery status, risks, blockers, quality metrics, and adoption indicators to stakeholders and leadership.
• Partner with compliance, security, and risk teams to ensure alignment with governance requirements.
• Drive data readiness planning and milestone management for enterprise initiatives, including migrations off legacy marketing automation platforms (e.g., Eloqua, Marketo) onto the Adobe stack.
• Develop and maintain POD performance metrics, including capacity utilization, velocity, delivery health, and quality scorecards.
• Serve as the trusted advisor for all data-related initiatives and capabilities within the Adobe ecosystem.
• Translate complex technical concepts into clear business outcomes for Product Managers, marketers, and executive stakeholders.
• Partner with business and technology teams to establish data ownership models, access strategies, service-level agreements, and governance standards.
• Identify opportunities to improve operational efficiency through process optimization, automation, and modernization initiatives.
• Drive enablement and knowledge transfer efforts that support long-term client ownership and operational maturity.
• Coach and develop team members while promoting a consultative, solution-oriented culture across the engagement.
• Manage escalations, provide strategic guidance, and partner with leadership to ensure successful solution delivery.

  Data Governance & Quality

  • Own the data governance roadmap and support enterprise governance initiatives.
  • Establish and maintain frameworks for data quality, freshness, completeness, lineage, and compliance monitoring.
  • Drive implementation of consent management strategies, data labeling standards, DULE policies, and governance controls within Adobe Experience Platform.
  • Develop and maintain KPI frameworks and dashboards that measure data health, trust, adoption, and operational effectiveness.
  • Drive data quality initiatives focused on freshness SLAs, completeness metrics, integration health monitoring, and operational reliability.
  • Partner with stakeholders to continuously improve data quality and reliability across customer experience platforms.

    Agentic Automation & Innovation

    • Identify and prioritize automation opportunities across data collection, integration, governance, and operational workflows.
    • Partner with AI and engineering teams to develop and scale automation initiatives that improve efficiency and reduce manual effort.
    • Establish governance controls and human-in-the-loop processes for AI-assisted workflows.
    • Drive initiatives focused on automated data validation, anomaly detection, schema mapping, and integration acceleration.
    • Measure and report automation outcomes, including operational efficiency gains, manual effort reduction, cost savings, and operational scalability.