Lead Enterprise Data Architect

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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