Data Architect

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.
•  Establish practical standards and ways of working that improve consistency, ownership, execution, and reuse across data initiatives.
•  Build strong working relationships with business and technical stakeholders, communicate clearly with senior leadership, and manage expectations across competing priorities.
•  Act as a senior point of accountability for data architecture and delivery decisions, while working through cross-functional teams rather than relying on direct people management.
•  Work effectively in manufacturing and industrial environments, with an understanding of operational data, plant processes, equipment, production, supply chain, or other industrial data domains.
•  Coordinate and influence cross-functional teams and partners to drive progress, remove blockers, and ensure commitments are delivered on time.
•  Drive strong data-operations practices covering data availability, reliability, quality, issue resolution, operational readiness, and continuous improvement.
•  Take ownership of data delivery outcomes by actively engaging in planning, problem solving, reviews, and execution rather than operating only as an advisory architect.
•  Provide technical and functional leadership across data initiatives, ensuring alignment between business needs, architecture, engineering, analytics, and operations.

Required Experience

Preferred Qualifications

• Experience with semantic modelling 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

Candidate Profile