BI Data Modeler - Remote

FERGUSON

1d•$65,337 - $114,444•Remote

About The Position

The Business Intelligence Data Modeler is part of Ferguson’s Enterprise Solutions organization, specifically the IT Enterprise Data Applications team. The role designs, builds, and optimizes data models and semantic layers that support enterprise reporting, forecasting, self-service analytics, and emerging AI and machine learning use cases. Working across business and technology teams, the modeler turns complex data into trusted, scalable, easy-to-use analytical solutions. The position is approved to be fully remote and may be based anywhere in the United States. It requires strong data modeling expertise, hands-on Power BI and SQL experience, and an understanding of modern enterprise data platforms and governance practices. The role also supports production semantic models, applies structured development and deployment practices, and prepares trusted data and business context for AI-related analytics.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Information Systems, Data Science, Data Analytics, Computer Engineering, or a related technical subject area, or equivalent relevant technical education and professional experience.
3–5 years of professional experience in business intelligence, data analytics, data modeling, semantic modeling, data warehousing, or a related data field.
Hands-on experience developing dimensional data models, such as star and snowflake schema structures, and translating business requirements into scalable analytical solutions.
Strong hands-on experience with Power BI, DAX, Power Query/M, and SQL, including developing and optimizing semantic models.
Experience working with enterprise data warehouses, data lakes, or other modern data platforms and integrating data from different sources.
Working knowledge of data quality, testing, security, governance, lineage, and enterprise reporting practices.
Experience with source control and structured development practices, including Git, peer review, environment promotion, and deployment pipelines.
Strong analytical and problem-solving skills, with the ability to communicate complex data concepts to technical and non-technical business customers.

Nice To Haves

Experience with Databricks, including an understanding of Medallion Architecture (Bronze, Silver, and Gold layers) and Unity Catalog.
Experience with semantic-layer or metrics technologies such as dbt, AtScale, Kyligence, or similar platforms.
Experience supporting enterprise-scale self-service BI and modern cloud data architectures.
Experience with planning, forecasting, scenario modeling, or advanced analytical use cases.
Exposure to AI, machine learning, semantic search, LLM-based analytics, or preparing curated enterprise data for AI consumption.
Familiarity with TMDL, XMLA endpoints, REST APIs, PowerShell, MDX, or BI/data platform automation.
Relevant Microsoft, Databricks, cloud, data, analytics, or business intelligence certifications.

Responsibilities

Design, build, and maintain conceptual, logical, physical, dimensional, and semantic data models, including star and snowflake schemas, conformed dimensions, bridge tables, and slowly changing dimensions.
Translate business and analytical requirements into scalable, reusable data models, standardized metrics, KPIs, calculations, and reporting definitions.
Develop and maintain Power BI semantic models, dashboards, reports, and curated analytical datasets.
Create calculations, queries, and data transformations using DAX, SQL, and Power Query/M.
Build planning and analytical models supporting prediction, scenario analysis, sensitivity analysis, and business decision-making.
Profile, validate, reconcile, and test data for accuracy, consistency, quality, and analytical readiness.
Optimize model performance, including storage modes, aggregations, partitions, incremental refresh, query performance, memory usage, and scalability.
Integrate data from enterprise data warehouses, data lakes, APIs, and other sources in partnership with data engineering and platform teams.
Implement and support data security and governance practices, including Row-Level Security, Object-Level Security, sensitivity labeling, lineage, certification, and metric governance.
Support production semantic models and datasets through monitoring, troubleshooting, root-cause analysis, and issue resolution.
Apply source control, peer review, deployment, change management, and rollback practices across BI and data modeling solutions.
Prepare trusted datasets, metadata, metric definitions, and business context for AI, machine learning, semantic search, AI-powered assistants, and LLM-based analytics.
Partner with technical and business stakeholders to capture requirements and maintain model diagrams, data dictionaries, user documentation, metric definitions, and support runbooks.
Adhere to Company compliance and Health, Safety and Environment (HSE) policies, procedures, and requirements.
Maintain regular and reliable attendance.
Perform other duties as assigned.