Ascentt is building cutting-edge data analytics & AI/ML solutions for global manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring passionate builders to shape the future of industrial intelligence.
Job Description: Data Architect - Data & Semantic Modeling Role Overview 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.
Key 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.
Required Experience - 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, Google Cloud Platform, 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.
Preferred Qualifications - 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.
Key Deliverables - The Data Architect will be responsible for producing and maintaining:
- Conceptual, logical, physical, and semantic data models
- Dimensional models and subject-area models
- Business metric and KPI definitions
- Data dictionaries and data glossaries
- Source-to-target mappings
- Entity relationship diagrams
- Data lineage and metadata documentation
- Modeling standards and best practices
- Data architecture design documents
- Curated consumption-layer design patterns
Candidate Profile The ideal candidate is a business-oriented data architect who can bridge the gap between business meaning and technical implementation. They should be able to lead modeling discussions, challenge unclear requirements, define reusable business entities, and create trusted data structures that support enterprise reporting, analytics, and modern data products.
This role is best suited for someone who has strong modeling depth, modern platform awareness, and the ability to create governed, scalable, and business-friendly data solutions.
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