Data Architect / Data Modeler

Ascentt•Plano, TX

13h

About The Position

We are seeking an experienced Data Architect with a strong focus on enterprise data modeling, semantic modeling, and modern data platform architecture. The ideal candidate will have a minimum of 10 years of experience designing scalable data solutions, building conceptual, logical, physical, and semantic models, and enabling trusted analytics across the enterprise. This role requires a candidate who can translate business requirements into reusable data structures, define consistent business metrics, and partner with engineering, analytics, governance, and business teams to create a strong data foundation. The candidate should have hands-on experience with modern cloud data platforms, lakehouse architecture, data warehousing, BI platforms, and governed data consumption layers.

Requirements

Minimum 10 years of experience in data architecture, data modeling, data warehousing, business intelligence, enterprise analytics, or related areas.
Strong hands-on experience in conceptual, logical, physical, and semantic data modeling.
Deep understanding of dimensional modeling concepts including facts, dimensions, grain, slowly changing dimensions, conformed dimensions, hierarchies, and metric design.
Experience designing semantic layers or business consumption layers for enterprise reporting and self-service analytics.
Strong SQL skills with the ability to validate models against source data, business rules, and reporting requirements.
Experience with modern cloud data platforms such as Databricks, Snowflake, Azure, AWS, GCP, Microsoft Fabric, or similar.
Experience with BI and analytics tools such as Power BI, Tableau, Looker, Qlik, or similar.
Strong understanding of metadata management, data lineage, governance, data quality, master data, and reference data concepts.
Ability to engage with business stakeholders, data engineers, BI developers, product owners, and governance teams.
Strong documentation, communication, problem-solving, and architecture leadership skills.

Nice To Haves

Experience with semantic modeling tools or frameworks such as Power BI semantic models, AtScale, Looker, dbt Semantic Layer, Tableau semantic layer, or similar.
Experience with governance and catalog tools such as Collibra, Alation, Informatica, Microsoft Purview, Unity Catalog, or similar.
Experience with data mesh, data products, domain-driven architecture, and enterprise metric stores.
Exposure to graph modeling, business ontology, metadata-driven architecture, or knowledge graph concepts is a plus.
Experience supporting AI/ML or GenAI use cases through well-governed and analytics-ready data models is preferred.
Industry experience in healthcare, manufacturing, financial services, retail, supply chain, automotive, or logistics is a plus.

Responsibilities

Design and maintain conceptual, logical, physical, and semantic data models to support reporting, analytics, operational, and advanced data use cases.
Define scalable data modeling patterns including dimensional models, star schemas, snowflake schemas, canonical models, entity relationship models, data vault concepts, and curated consumption-layer models.
Develop semantic models that establish consistent business definitions, KPIs, metrics, hierarchies, dimensions, and calculation logic across analytics and BI platforms.
Work with business stakeholders to understand processes, define business terms, identify key data domains, and convert requirements into clear and governed data models.
Partner with data engineering teams to ensure data models are accurately implemented across data pipelines, warehouses, lakehouses, and semantic layers.
Establish standards for naming conventions, keys, relationships, metadata, data lineage, data quality rules, and modeling documentation.
Support modern data platform architecture across technologies such as Databricks, Snowflake, Azure Synapse, Microsoft Fabric, AWS Redshift, Google BigQuery, Delta Lake, or similar platforms.
Design models across raw, curated, and consumption layers, including bronze/silver/gold or equivalent lakehouse patterns.
Review and optimize existing models for performance, scalability, usability, consistency, and maintainability.
Support data product design by defining domain-aligned entities, data contracts, reusable metrics, and governed consumption models.