Senior Associate Manager, Digital Customer Data and Engagement Solutions

The Day-to-Day

Customer Data Architecture & Activation Readiness

• Lead the solution approach for customer data used in CRM and digital engagement, including customer signals, attributes, events, audience inputs, identity mappings, consent, and downstream activation requirements.

• Assess source-of-truth, data type, coverage, timeliness, grain, and one-to-one mapping before data is operationalized for activation, partnering with the appropriate data owners to validate assumptions.

• Define repeatable activation patterns for moving trusted data through CDP/warehouse, Braze, and related platforms without creating unnecessary one-off implementations.

• Proactively identify data quality, schema, identity, latency, and integration gaps and coordinate resolution with the appropriate owning teams, escalating when decisions or tradeoffs are required.

Engagement Solution Architecture & Enablement

• Own technical solution design for CRM, lifecycle, loyalty, personalization, and experimentation workflows across email, push, in-app, SMS, and offer constructs.

• Define reusable activation patterns, segmentation approaches, orchestration standards, QA/release controls, and implementation guardrails that improve consistency, quality, and speed to market.

• Serve as a senior technical authority for how engagement solutions are designed and activated, balancing scalable architecture with practical delivery needs.

• Guide complex or non-standard campaign designs and act as the escalation point for solution decisions that cross data, platform, and channel boundaries.

Data, Identity & Integrations

• Partner with Data Engineering, Middleware, Analytics, and platform teams on APIs, webhooks, event schemas, data contracts, identity resolution, consent, and operational data flows.

• Validate whether existing workflows and platform capabilities can support a requirement before recommending new pipelines, custom events, or net-new architecture.

• Use data and technical analysis to diagnose issues, determine the appropriate owner, and distinguish activation work from deeper analytics or data-science needs.

BAU Enablement & Operating Model

• Own or shape the CDS operating model for BAU activation, including intake, complexity triage, review triggers, QA/release readiness, launch validation, and post-launch learnings.

• Create clear ownership and escalation paths across CDS, Lifecycle, Loyalty, Data, Analytics, Engineering, and agency/platform partners.

• Continuously identify opportunities to simplify, automate, standardize, or shift work to the right operating team so CDS can scale without unnecessary manual effort.

AI Decisioning, Personalization & Automation

• Translate AI decisioning outputs, recommendations, scores, and dynamic content into activation-ready customer data and orchestration patterns.

• Partner on Braze AI Decisioning and agentic workflows with appropriate controls, monitoring, human-in-the-loop review, and operational guardrails.

• Prioritize AI and automation opportunities that build on stable data and reusable activation patterns rather than creating unnecessary bespoke complexity.

Cross-Functional Leadership

• Lead through influence across Product Management, Data Engineering, Middleware, Analytics/BI, Lifecycle Marketing, Loyalty, Engineering, and agency/platform partners.

• Bring forward recommendations, tradeoffs, risks, and decisions without waiting for every next step to be assigned.

• Communicate complex data and architecture topics clearly to both technical and non-technical stakeholders and create durable documentation that enables others to operate independently.

Team Development & Capability Building

• Mentor junior team members and raise the team's technical capability through playbooks, reusable patterns, documentation, and knowledge sharing.

• Help establish standards that reduce dependency on individual knowledge and improve resilience when priorities, partners, or staffing change.

• May manage 1-2 direct reports depending on team needs and operating model.

What Success Looks Like

• Can independently take an ambiguous customer-data or engagement problem, clarify the requirement, identify the authoritative data source, assess feasibility, and recommend the right path forward.

• Is recognized by xFN partners as a primary technical owner for defined customer data and engagement workstreams.

• Builds reusable patterns and operating standards that reduce rework, manual review, and dependency on individual team members.

• Raises risks early, proposes options, and moves work forward without requiring frequent manager intervention.

• Creates clear data and solution documentation that allows CDS, agency, and partner teams to execute consistently.