Job Description

Why This Role Matters

This role leads the design, modeling, and evolution of enterprise data architecture to ensure data products, curated layers, and integration patterns are standardized, scalable, high-quality, and governed. It defines how operational and corporate data should be modeled, aligned, and transformed to support analytics, reporting, AI enablement, and trusted downstream consumption across business domains.

What You Will Deliver

Lead Enterprise Data Modeling

Develop and maintain conceptual, logical, and physical data models aligned with enterprise business domains and data architecture principles.

Define canonical models, conformed dimensions, master/reference data structures, and reusable modeling patterns for cross-domain consistency.

Design data product models and curated layer schemas that support enterprise reporting, analytics, AI use cases, and downstream integrations.

Ensure models address performance, maintainability, scalability, auditability, lineage, and future reuse across platforms and domains.

Define Architecture Standards and Governance

Establish enterprise modeling standards, naming conventions, modeling guidelines, reusable templates, and quality expectations for delivery teams.

Partner with data governance teams to align business glossary definitions, stewardship ownership, metadata, lineage, and critical data elements.

Embed governance-by-design into data models, curated layers, transformation logic, data quality controls, and consumption patterns.

Review and approve modeling deliverables from internal teams and external vendors to ensure architectural consistency and quality.

Shape Data Product and Curated Layer Architecture

Lead the creation and optimization of data product schemas, curated layers, semantic structures, and consumption-ready models.

Define modeling approaches for relational, non-relational, time-series, streaming, lakehouse, warehouse, and analytical platform requirements.

Ensure data models support time-variant structures, historical traceability, auditability, semantic consistency, and trusted analytics consumption.

Coordinate with data engineering, platform, analytics, AI, and enterprise architecture teams to ensure models are technically feasible and reusable.

Enable Integration, Interoperability and Trusted Consumption

Define integration patterns that ensure consistent transformation logic, metadata, lineage, and data quality across source-to-consumption pipelines.

Align operational, corporate, and analytical data structures to enable reliable reporting, analytics, AI enablement, and downstream system integration.

Identify and resolve modeling inconsistencies, duplication, semantic gaps, and integration challenges across domains and platforms.

Ensure data is modeled for trusted consumption by business users, data engineers, analysts, data scientists, and application teams.

Provide Technical Leadership and Modeling Assurance

Act as a senior technical authority for data modeling and architecture decisions across enterprise data initiatives.

Mentor data modelers, data engineers, analysts, and delivery teams on modeling standards, patterns, review practices, and quality expectations.

Lead model reviews, resolve design trade-offs, and guide teams on semantic consistency, reusability, performance, and maintainability.

Document architecture decisions, model assumptions, standards exceptions, and reusable design guidance for enterprise adoption.

What Success Looks Like

Enterprise conceptual, logical, and physical data models are standardized, scalable, reusable, and aligned with business domains.

Canonical models, conformed dimensions, reference data structures, and naming standards improve semantic consistency across domains.

Curated layers and data product schemas support trusted reporting, analytics, AI enablement, and downstream consumption.

Metadata, lineage, data quality, stewardship, and governance expectations are embedded into modeling and integration patterns.

Delivery teams receive strong modeling assurance, coaching, and practical design guidance that reduces duplication and improves reuse.

Minimum Qualifications

Bachelor’s Degree in Computer Science, Computer Engineering, Data Engineering, Information Systems, Software Engineering, Analytics, or a related discipline. Master’s Degree in Data Architecture, Software Engineering, Analytics, or a related field is preferred.

Experience

Minimum 10–15 years of experience in data modeling, data architecture, enterprise information architecture, data engineering, or data platform delivery.

Strong experience developing conceptual, logical, and physical models, canonical models, conformed dimensions, curated layer schemas, and reusable data product structures.

Demonstrated experience defining modeling standards, data architecture patterns, metadata, lineage, governance controls, and quality assurance for enterprise data initiatives.

Skills That Matter

Enterprise data modeling: conceptual, logical, physical, canonical, dimensional, semantic, and reference data models

Data architecture patterns for curated layers, data products, lakehouse, warehouse, relational, non-relational, streaming, and time-series platforms

Data governance, metadata, lineage, stewardship, business glossary alignment, data quality, and auditability

Conformed dimensions, master and reference data, semantic consistency, naming standards, and reusable modeling templates

Source-to-consumption pipelines, transformation logic, integration design, interoperability, and trusted analytics consumption

Model performance optimization, scalability, maintainability, lifecycle management, and architectural assurance

Ability to review vendor and internal modeling deliverables and guide multidisciplinary delivery teams

Clear communication of modeling decisions, architecture trade-offs, standards, risks, and design recommendations