Job Title: Data 360 Architect - Level 1
Contract Duration: 12 months
Location: 100% Remote – Anywhere in the US
Schedule: EST (Eastern Time)
Interviews: ASAP | Maximum 3 rounds
About the Role
We are building Data'360 into the connected data foundation for our entire go-to-market motion — the single trusted source that powers analytics, personalization, activation, and the next generation of AI agents across a $2B revenue organization.
This role owns the blueprint. You will design the enterprise data model, unify fragmented source systems into resolved and governed profiles, and make that data usable in real time by marketers, sellers, analysts, and agents. You are not a report builder or a campaign operator; you are the architect who decides how data is structured, where it lives, how it moves, and how it earns trust.
This is a high-leverage seat. The decisions you make in the first two quarters will constrain or unlock what our GTM organization can do for the next several years.
What You'll Own
Data Modeling in Data'360
• Design and maintain the canonical data model — data model objects, relationships, data graphs, and calculated insights — as a governed contract between source systems and downstream consumers.
• Define mapping standards from source objects to standard and custom DMOs, and enforce them across incoming integrations.
• Balance normalization against query performance and activation latency, and document the reasoning behind each trade-off.
• Own model versioning and change management so downstream segments, dashboards, and agents don't break when the model evolves.
Design and Unification of Enterprise Data
• Architect ingestion across CRM, marketing automation, web and product telemetry, sales engagement, support, finance, and third-party intent and enrichment sources.
• Design identity resolution: match rules, reconciliation logic, and survivorship across person, account, household, and product hierarchies — including the hard cases where B2B and B2C identity models collide.
• Decide streaming versus batch versus zero-copy federation for each source, based on latency requirements and cost, not habit.
• Establish the unified profile as the definitive representation of a customer, and defend it against the pull toward system-specific truth.
Real-Time Insights for AI, Analytics, and Personalization
• Design low-latency access patterns that support in-session personalization, real-time scoring, and streaming calculated insights.
• Make trusted data available to analytics and data science teams without forcing duplication — federation and shared semantics over yet another copy.
• Partner with data science to ensure features and signals required by models are available, documented, and stable.
• Define and hold latency and freshness SLAs by use case; not everything needs to be real time, and you'll be the one who says so.
Activation and Signal Response
• Architect activation to paid media, marketing automation, sales engagement, web and app personalization, and internal operational systems.
• Design event-driven signal response: the pipes and logic that turn a behavioral or firmographic signal into a triggered action within a defined time window.
• Enforce consent, suppression, and regional privacy rules at the activation layer so compliance is structural rather than procedural.
• Close the loop — ensure activation outcomes flow back into the platform to make measurement and attribution possible.
Segmentation for System and Business Strategy
• Build a reusable, governed segment library with clear naming, ownership, lifecycle, and deprecation standards.
• Enable marketers to self-serve on segmentation without creating sprawl, redundancy, or conflicting definitions of the same audience.
• Design the segmentation layer to serve two masters: business strategy (ICP, lifecycle stage, propensity, territory) and system logic (routing, suppression, orchestration triggers).
• Provide audience overlap, reach, and quality analysis so segment design is an evidence-based decision.
Database Health Within and Outside Data'360
• Define data quality standards — completeness, accuracy, timeliness, uniqueness — and instrument monitoring against them.
• Drive remediation upstream at the source of record rather than patching symptoms inside the CDP.
• Own deduplication, normalization, and enrichment strategy across CRM and marketing automation, not just within Data'360.
• Build observability and alerting for pipeline failures, schema drift, identity resolution degradation, and anomalous volume.
• Manage platform consumption and cost, treating credits and storage as a resource you are accountable for.
Positioning Data'360 as the Core CDP
• Author and maintain the reference architecture showing how Data'360 sits relative to the data warehouse, CRM, MAP, analytics, and activation endpoints.
• Identify and retire overlapping capabilities across the existing stack; consolidation is an explicit goal of this role.
• Establish integration patterns and standards that new tools must conform to in order to enter the ecosystem.
• Partner with Enterprise Data, IT, Security, and Legal on governance, access control, retention, and data residency.
• Influence senior stakeholders across Marketing, Sales, and Revenue Operations — much of this role's impact depends on alignment you build rather than systems you configure.
Agent Building
• Ground AI agents in trusted, permissioned data — designing the retrieval, context, and data access patterns agents depend on.
• Define agent topics, actions, and guardrails for GTM use cases such as account research, next-best-action, campaign QA, and data stewardship.
• Establish evaluation and monitoring practices so agent behavior is measured rather than assumed, including human-in-the-loop checkpoints where stakes warrant it.
• Sequence agent work realistically against data readiness — agents built on unresolved identity and poor data quality fail, and you'll be the one holding that line.
What You Bring
Required
• 1- 3 years in data architecture, marketing technology, or revenue systems, including 3+ years architecting on a CDP or customer data platform in a production enterprise environment.
• Demonstrated ownership of an enterprise data model — you have designed one, defended it, and lived with the consequences.
• Deep, practical experience with identity resolution in a complex environment with imperfect source data.
• Strong SQL and fluency with modern data platforms (Snowflake, BigQuery, Databricks, or equivalent), including federation and zero-copy patterns.
• Working knowledge of the GTM stack: CRM, marketing automation, web analytics, sales engagement, and ad platform integration.
• Applied understanding of privacy and consent frameworks (GDPR, CCPA/CPRA) and how they constrain data architecture in practice.
• The ability to explain architectural trade-offs to a VP of Marketing and to a data engineer in the same meeting, and be credible to both.
Preferred
• Salesforce Data Cloud or Data'360 certification, comparable depth on Adobe Experience Platform, Segment, or Treasure Data.
• Hands-on experience building or grounding AI agents in enterprise data, including evaluation and guardrail design.
• Experience leading a CDP implementation or platform consolidation from evaluation through production.
• Background in B2B or hybrid B2B/B2C demand generation and attribution.
• Experience working alongside enterprise data and IT organizations on shared governance.
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