Onsite_Data Architect- MS Fabric_

Job Title: Data Architect - MS Fabric
Location: Columbus, IN
Duration: 6-12+ months
Contract

Role Summary

Seeking a Data Modeling Architect to define and implement the enterprise modeling framework for an advanced data fabric architecture. This role translates business capabilities, domain concepts, and source-system structures into governed conceptual, logical, physical, and semantic models.
The architect establishes reusable data contracts and canonical definitions that support interoperability, discoverability, trusted data products, and analytics across Microsoft Fabric and connected enterprise platforms. The role requires strong data modeling expertise and hands-on implementation experience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Pipelines, and Power BI semantic models.
The architect works directly with business domain owners and the Data Team to align models with enterprise architecture standards, operational processes, and measurable business outcomes.

Key Responsibilities

Enterprise Ontology and Domain Modeling
Define an enterprise ontology and domain taxonomies that establish consistent business terminology, entity definitions, relationships, and business rules across functional domains.
Develop conceptual and logical models that capture domain boundaries, shared entities, reference data, cardinality, hierarchies, and cross-domain relationships.
Select appropriate modeling patterns, including normalized entity-relationship models, dimensional models, Data Vault, and canonical data models, based on integration and consumption requirements.
Define authoritative sources, system-of-record relationships, data ownership, and master and reference data alignment for shared business entities.
Data Fabric and Physical Implementation
Design physical models for Microsoft Fabric Lakehouse and Warehouse workloads, including Delta table structures, partitioning strategies, keys, schema evolution, and query-performance considerations.
Design and govern open table-format implementations using Apache Iceberg, Apache Hudi, and Delta Lake across lakehouse workloads.
Select the appropriate table format based on interoperability, transaction requirements, ingestion patterns, query-engine compatibility, performance, cost, governance requirements, and operational supportability.
Define physical table-design standards for partitioning, clustering or data layout, compaction, file sizing, retention, schema evolution, partition evolution, and metadata management.
Establish patterns for ACID transactions, snapshot isolation, time travel, rollback, auditability, and reproducible data consumption across curated data products.
Define incremental-processing patterns using change data capture, merge and upsert operations, deletes, deduplication, slowly changing dimensions, and late-arriving data handling.
Define table-maintenance and optimization processes, including compaction, small-file remediation, orphan-file cleanup, snapshot expiration, retention controls, and performance monitoring.
Define interoperable data product schemas and data contracts to support governed exchange across ingestion, transformation, serving, and consumption layers.
Design source-to-target mappings and transformation specifications for ERP, CRM, operational systems, APIs, and other enterprise data sources.
Apply bronze, silver, and gold layer patterns while defining fit-for-purpose data products and reusable integration assets across OneLake, Lakehouse, and Warehouse.
Work with engineering teams to implement batch and near-real-time integration patterns, including incremental ingestion, change data capture, and event-driven data flows where appropriate.
Define standards for schema versioning, backward compatibility, and model lifecycle management as source systems and business requirements evolve.
Establish cross-platform interoperability standards for data products consumed through Microsoft Fabric, Databricks, Snowflake, Spark, Trino, and compatible query engines.
Semantic Layer, Metadata, and Governance
Design governed Power BI and Fabric semantic models with conformed dimensions, consistent measures, calculation logic, and business-friendly terminology.
Establish metadata requirements for business glossaries, technical catalogs, lineage, classifications, ownership, and policy enforcement using Microsoft Purview or equivalent capabilities.
Align models with data quality rules, validation controls, observability requirements, and master data management practices.
Maintain traceability from business concepts and data contracts through source-to-target mappings, physical assets, and semantic models.
Partner with security teams to account for role-based access, sensitivity labels, and appropriate data access controls in model design.
Business Partnership and Team Standards
Facilitate working sessions with business stakeholders, domain owners, architects, and delivery teams to validate definitions, resolve cross-domain conflicts, and document design decisions.
Provide architectural guidance and design reviews for data engineering, analytics, and integration initiatives across TMHNA.
Contribute to work standardization across the Data Team by defining modeling conventions, reusable templates, review checklists, repository practices, and implementation patterns.
Establish shared standards for open table formats, including table-format selection criteria, metadata governance, lifecycle management, and migration approaches.
Mentor architects, engineers, and analysts on domain modeling, semantic consistency, data contracts, open table formats, and Microsoft Fabric design practices.

Required Qualifications

10 or more years of experience in data architecture or data engineering, including at least four years of hands-on conceptual, logical, and physical data modeling.
Experience defining enterprise ontologies, domain models, shared business vocabularies, and governed semantic models.
Hands-on experience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2, and semantic models, or comparable experience with Microsoft data platforms such as Synapse, Azure Data Factory, and Azure SQL.
Strong knowledge of entity-relationship modeling, dimensional and star-schema modeling, Data Vault, and canonical modeling patterns.
Experience defining data contracts, source-to-target mappings, schema evolution rules, and integration specifications across heterogeneous platforms.
Hands-on experience designing, implementing, or governing lakehouse tables using at least one of Apache Iceberg, Apache Hudi, or Delta Lake, with working knowledge of the other formats and their architectural tradeoffs.
Strong understanding of open table-format capabilities, including ACID transactions, snapshots, time travel, schema evolution, partition evolution, metadata files, file management, and concurrent read and write behavior.
Experience designing incremental data-processing patterns, including change data capture, merge and upsert logic, deduplication, deletes, late-arriving data, and slowly changing dimensions.
Experience establishing operational standards for table maintenance, including compaction, clustering or layout optimization, file sizing, retention, cleanup processes, and snapshot management.
Knowledge of how table formats interact with distributed compute and query engines such as Spark, Microsoft Fabric, Databricks, Snowflake, Trino, or equivalent platforms.
Ability to evaluate table-format selection based on workload type, streaming versus batch ingestion, cross-engine access, governance requirements, performance, cost, and operational complexity.
Practical experience with Delta Lake, medallion architecture, and lakehouse data design.
Knowledge of metadata management, business glossaries, technical lineage, data catalogs, and policy-driven governance.
Experience with data modeling tools such as erwin, ER/Studio, SQLDBM, Lucidchart, or equivalent tools.
Ability to explain architectural decisions and tradeoffs to business and technical stakeholders and drive alignment across domains.
Experience developing shared modeling standards, conducting design reviews, and working across concurrent enterprise initiatives.


Tools and Technologies
Data fabric architecture
Data products, data contracts, domain-oriented design, metadata-driven integration, interoperability, lineage, policy enforcement, cross-engine data access
Microsoft Fabric
OneLake, Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2, semantic models, workspace and capacity concepts
Open table formats
Apache Iceberg, Apache Hudi, Delta Lake, ACID transactions, snapshots, time travel, rollback, schema evolution, partition evolution, compaction, retention, file lifecycle management
Lakehouse engineering
Delta tables, Parquet, table metadata, transaction logs, manifest files, merge and upsert patterns, change data capture, slowly changing dimensions, incremental processing
Modeling
Enterprise ontology, domain and canonical models, entity-relationship modeling, dimensional modeling, Data Vault, schema evolution
Integration and engineering
Azure Data Factory, Synapse, Spark, Databricks, Snowflake, APIs, change data capture, event-driven integration, batch and near-real-time processing
Analytics and semantic layer
Power BI, DAX, conformed dimensions, governed measures, Tableau
Governance and security
Microsoft Purview, business glossary, data catalog, lineage, Entra ID, role-based access control, sensitivity labels, table ownership, schema versioning, quality controls, observability, retention policies
Cloud platform
Microsoft Azure


Work Authorization
Applicants must be legally authorized to work in the United States.

Equal Employment Opportunity
Themesoft Inc is an Equal Employment Opportunity employer. We consider qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by applicable federal, state, or local law.