Senior Data Architect – Data Foundation, AI Data Readiness

Phase2

. Help pitch and sell enterprise data capabilities with sales and account teams .

Posted 11/9/2026full-timeRemote • United StatesSenior💰 $132,000 - $155,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering and architecture, with a strong focus on establishing data quality, integration, and modeling practices. Proficient in translating business needs into scalable data solutions while ensuring compliance and effective communication with stakeholders.

Highest-signal resume keywords

Data EngineeringData ArchitectureSQL FluencyData Quality PracticesExecutive Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingIntegrationTransformationStoragePrototypingData ValidationData GovernanceMetadata ManagementAI ApplicationsData Pipelines

Soft Skills

StorytellingJudgmentCollaborationGeneralist OrientationStakeholder Management

Industry Keywords

KimballData VaultComplianceRegulatory AuditsData CatalogEnterprise AnalyticsAI Solution DeliverySemantic StandardsContext LayersData Ecosystems

Tech Stack

Tools & technologies

SQLVault

About the role

Key responsibilities & impact

Help pitch and sell enterprise data capabilities with sales and account teams
Lead technical client conversations and diagnose underlying data challenges
Translate business needs into practical data solutions
Quantify cost avoidance, reporting-time savings, and eliminated duplicate work
Establish measurable outcomes and communicate results through demos and executive updates
Establish reusable data architectures, technical standards, and delivery approaches
Align semantic standards and warehouse architecture with marketing data work including CDP, attribution, and activation
Establish semantic and context layers across client data ecosystems
Resolve semantic and contextual gaps affecting consistency, accessibility, and reliability
Design and deliver metadata, lineage, and data quality practices
Define scalable data access and integration patterns for analytics, AI applications, copilots, and agents
Architect, build, and optimize data platforms and pipelines covering ingestion, integration, transformation, modeling, and storage
Deliver proofs of concept and production-ready data solutions across diverse client environments
Collaborate with software engineers and technical specialists on implementation and reusable engineering patterns
Establish data quality, validation, and testing practices
Define technical standards and review data solutions developed by engineers and AI-assisted tooling
Incorporate documentation, metadata, lineage, governance, security, compliance, auditability, and responsible data use practices
Help establish reusable data architecture, engineering, and AI-readiness frameworks for Phase2

Requirements

What you’ll need

8+ years of experience in data engineering, data architecture, or a closely related discipline
Experience establishing and applying data quality, testing, and validation practices
Ability to prototype and build solutions across the data and application stack
Experience architecting and delivering production data platforms and solutions, including integration, transformation, modeling, and storage
Fluency in SQL and data modeling
Strong judgment regarding Kimball, Data Vault, and other modeling approaches
Ability to trace data-related complaints to underlying data root causes
Experience navigating complex stakeholder environments and balancing technical, business, security, and compliance considerations
Strong executive communication and storytelling skills
Experience delivering visible progress in short cycles
Generalist orientation and comfort with ambiguity
Nice to have: experience supporting regulatory audits or compliance reviews in regulated industries
Nice to have: experience bridging data product management and AI solution delivery
Nice to have: experience with metadata management and data catalog ecosystems
Nice to have: full-stack engineering experience
Nice to have: experience building or governing controls and metrics layers for enterprise analytics at scale
Nice to have: experience directing AI agents or agentic tooling
Travel, sometimes frequent, is expected

Benefits

Comp & perks

Remote-first work from wherever you thrive best
Medical, dental, vision, life insurance, disability coverage, and supplemental insurance
Wellness program with participation rewards
401(k) with 3% employer contribution; no match required; 100% vested after 90 days
16 paid vacation days, 4 personal days, and 1 volunteer day each year
Vacation and personal time increase with tenure
Up to 12 weeks of fully paid leave for birthing parents and up to 6 weeks of fully paid bonding leave, based on tenure
Adoption and foster placement support
Option to extend leave through additional unpaid leave
$500 home workspace stipend
Competitive compensation package
Opportunity to make a real impact helping millions of people per year