Lead Enterprise Data Architect

Title: Lead Enterprise Data Architect

Location: Saint Louis United States

Charlotte, NC, United States 

and 2 more 

Charlotte, NC, United States
Irving, TX, United States
Melville, NY, United States
(Hybrid)
(Hybrid)

• Job Identification18263
• Job CategoryData Management
• Posting Date04/21/2026, 10:47 PM
• Job ScheduleFull time
• Locations 2238 Ball Dr, St. Louis, MO, 63146, US(Hybrid)
• Incentive EligibleRBP
• BusinessADI Global Distribution
• Hiring Salary RangeThe typical hiring salary for this role, ranges from USD $133263.64 to $198821.82 per year but varies by specific work location. For example, the hiring salary for this role in Melville, NY is $159916.36 to $238586.18 per year and New York City, NY is $166579.55 to $248527.27 . Within a range, Resideo determines base pay for an individual based on various factors, including market conditions, skills, and experience.
• Incentive Eligible (RBP)This position is eligible for a performance-based bonus of up to 10% of the annual base salary. The bonus is contingent upon both individual and company performance.
• BenefitsResideo provides comprehensive benefits, including life and health insurance, life assistance program, accidental death and dismemberment insurance, disability insurance, 401k Plan, vacation & holidays.

Job Description

Job Description:

Enterprise Data Architect

We are seeking an experienced and passionate Enterprise Data Architect to build and own foundational enterprise data management capabilities spanning Master Data Management (MDM), Data Governance, Data Quality, Metadata & Cataloging, semantic/context layer engineering, and enterprise data architecture. This role combines strategic leadership with hands‑on technical expertise to ensure enterprise data is trusted, governed, discoverable, and ready for analytics, AI, and operational use.

The Enterprise Data Architect designs, governs, and evolves the enterprise-wide data architecture that powers analytics, AI, and operational workflows. You will define standards and reference architectures; guide data modeling and integration patterns; and influence platform decisions across the enterprise data hub/warehouse ecosystem, MDM, governance, and metadata capabilities.

JOB DUTIES

Enterprise Data Architecture Leadership

• Define and maintain the enterprise data architecture strategy, reference models, and standards
• Create and govern canonical data models, domain models, and integration patterns
• Ensure architectural alignment across data engineering, analytics, MDM, governance, and application teams
• Drive modernization toward cloud‑native, scalable, AI‑ready architectures
• Define architecture guardrails for data security, privacy, and regulatory compliance in partnership with Security and Legal (e.g., access controls, classification, retention)

Data Modeling & Canonical Design

• Lead design of conceptual, logical, and physical data models across domains
• Establish enterprise‑wide modeling standards, naming conventions, and modeling patterns
• Partner with MDM and governance teams to ensure consistency across master data, reference data, and operational data

Semantic / Context Layer Architecture

• Architect and maintain the enterprise context layer (semantic layer) enabling consistent metrics, definitions, and reusable data entities
• Define metric logic, dimensional models, and semantic relationships used across BI, AI, and operational systems
• Ensure alignment with analytics engineering (dbt, metric stores, semantic tools)

Master Data & Governance Architecture

• Architect MDM solutions including domain models, match/merge logic, hierarchies, and integration patterns
• Partner with governance teams to operationalize policies through technology
• Integrate metadata, lineage, and governance workflows into the architecture

Data Integration & Platform Architecture

• Define ingestion, transformation, and consumption patterns across batch, streaming, and API‑based pipelines
• Architect cloud data platforms (Azure/AWS/GCP) including lakehouse, warehouse, and real‑time components

Metadata, Catalog, and Lineage Architecture

• Ensure scalability, performance, security, and cost optimization
• Design metadata ingestion patterns and lineage frameworks across pipelines, BI tools, and MDM systems
• Implement enterprise cataloging solutions using platforms such as Collibra, Atlan, Alation, or similar
• Ensure metadata is complete, accurate, and actionable for governance and engineering teams

Hands‑On Technical Execution

• Build and validate architectural prototypes, POCs, and reference implementations
• Write SQL, design schemas, build lineage connectors, and define transformation logic
• Troubleshoot complex data architecture issues across pipelines, models, and platforms

Cross‑Functional Leadership

• Partner with data engineering, analytics, MDM, governance, product, and application teams
• Provide architectural guidance, code reviews, and technical mentorship
• Communicate architectural decisions to executives, engineers, and business stakeholders

YOU MUST HAVE

• 8+ years of experience in data architecture, data engineering, or enterprise architecture
• Deep hands‑on experience with cloud data platforms (Snowflake, Databricks, Azure, AWS, or GCP)
• Strong expertise in data modeling (dimensional, relational, canonical, semantic)
• Experience architecting MDM and governance solutions using Collibra, Reltio, Atlan, Informatica, or similar
• Strong SQL, data pipeline design, and metadata/lineage engineering skills
• Experience with modern data stack tools (dbt, Spark, Kafka, Airflow, etc.)
• Ability to translate business needs into scalable architectural designs
• Experience with enterprise architecture frameworks (TOGAF, DAMA‑DMBOK)
• Background in designing AI‑ready data architectures (feature stores, vector stores, semantic layers)
• Experience with API‑driven architectures and event‑driven patterns
• Familiarity with data products and data mesh concepts
• Adoption of standardized data models and architectural patterns across the enterprise
• Reduction in data duplication, inconsistencies, and integration complexity
• High‑quality, governed, discoverable data powering analytics and AI
• Scalable, cost‑efficient cloud data platform performance
• Strong alignment between business, engineering, and governance teams

WE VALUE

• Experience with enterprise architecture frameworks (TOGAF, DAMA‑DMBOK)
• Background in designing AI‑ready data architectures (feature stores, vector stores, semantic layers)
• Experience with API‑driven architectures and event‑driven patterns
• Familiarity with data products and data mesh concepts

Success Measures

• Adoption of standardized data models and architectural patterns across the enterprise
• Reduction in data duplication, inconsistencies, and integration complexity
• High‑quality, governed, discoverable data powering analytics and AI
• Scalable, cost‑efficient cloud data platform performance
• Strong alignment between business, engineering, and governance teams

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