市场部-顾客数据专家（CDP Data Expert）

What you will do

Data & Identity Solution Design

• Apply an Agent-ready mindset to data solution design: make data discoverable, well-defined, contextualized, governed and machine-readable, with clear definitions and fit-for-purpose interfaces that reduce ambiguity and improve reliable agent consumption.
• Connect the chain from business question → data requirement → data model → identity/customer view → analysis → insight → audience → activation → measurement, and identify where data design can improve downstream agent use.
• Translate use-case needs into precise data requirements, including required/optional attributes, events, historical vs. real-time needs and freshness expectations.
• Assess source-system readiness and data quality; identify gaps, remediation needs and onboarding priorities in partnership with source owners and engineering teams.
• Define and maintain identity strategy, identifier hierarchy, ID mapping and match/merge logic; monitor identity resolution quality and investigate exceptions.
• Establish practical data-quality checks across completeness, consistency, duplication and timeliness, and work with owners to resolve issues.
• Shape data lifecycle requirements such as retention, retirement and deletion in partnership with governance/privacy stakeholders.

Data Analysis & Insights

• Partner with the CDP Business Lead and BA role to translate insights into prioritized actions, new use cases, controlled changes or data-solution improvements.
• Design practical analytical approaches and baselines for CDP use cases, customer/account behaviors, journeys and activation outcomes.
• Analyze customer, account, journey data to identify patterns, behavioral signals, opportunities and risks.
• Investigate data relationships and behavioral trends to explain what is happening, why it may be happening, and where further analysis is needed.
• Turn complex analyses into concise, actionable insights and recommendations that help business and CDP stakeholders make better decisions.
• Build reusable analytical views, data cuts and narratives that enable stakeholders to answer recurring business questions without repeated manual data preparation.

Audience & Activation

• Own practical audience taxonomy and translate business logic into robust segment and audience definitions.
• Design eligibility, exclusion and suppression logic, including account/contact level considerations for B2B activation.
• Configure or partner on audience, journey and destination setup across email, web, CRM and other activation channels.
• Validate audience behavior and activation outcomes, then use performance data to improve audience and journey design.
• Maintain audience lifecycle discipline: ownership, reuse, review, expiration and retirement.

Qualifications & Experience

• 5+ years of professional experience in customer data, data analytics, data modeling. Familiar with CDP, CRM, data products or closely related fields.
• Strong hands-on data modeling experience, including the ability to design business-oriented customer/account data models and explain trade-offs clearly.
• Strong Python & SQL skills and experience independently exploring, validating and analyzing customer/account-level data to answer business questions.
• Practical experience with identity resolution, customer matching, ID mapping, data quality or comparable customer data management concepts.
• Ability to turn analysis into clear insights and recommendations, and translate those insights into audience, journey, activation or data-solution requirements.
• Strong communication and stakeholder-management skills; able to work effectively with technical, analytical and business teams.
• Understanding of how data should be structured, contextualized, governed, documented and exposed so AI agents can reliably discover, retrieve, interpret and use business data.

Preferred

Experience with Tealium, Salesforce, Adobe Real-time CDP or another CDP platform.

• B2B experience with account/contact relationships, ABM, buying groups, lead/opportunity or customer lifecycle data.
• Experience with modern data platforms (warehouse/lakehouse), semantic models or data transformation frameworks.
• Experience with cohort analysis, experimentation, attribution, propensity/scoring, behavioral analytics or advanced segmentation.
• Experience with semantic layers, metadata catalogs, knowledge graphs, data products, APIs / retrieval interfaces, AI agents or other approaches that improve machine consumption of enterprise data.
• Experience shaping data for AI / agentic workflows while maintaining clear semantics, access controls, lineage, discoverability and reliable retrieval.