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The Data Modeling Engineer builds dimensional models and Business Vault structures in Snowflake based on approved architectural designs and specifications. This SQL-first role applies approved business logic and KPI definitions to produce performant, documented, and governed data products that power BI, analytics, and AI use cases. The role partners closely with data engineering teams on upstream Data Vault inputs and with BI teams using Power BI to ensure accurate, reliable, and consumption-ready modeled data. Architectural design decisions are provided; this role focuses on implementation and build execution within Snowflake.
Job Responsibility:
Build architect-approved star schemas and subject-area dimensional models
Implement bridge tables resolve many-to-many relationships, hierarchies, and allocation logic prior to or within dimensional modeling
Apply established dimensional modeling patterns including surrogate keys, SCD Type 1/2, conformed dimensions, and approved bridge-table techniques
Implement approved KPI definitions, filters, and business rules into governed semantic views
Deliver business-friendly semantic layers optimized for Power BI consumption, standardizing metrics and hiding technical complexity
Apply Snowflake governance controls including data masking, sensitivity tagging, and (where applicable) row access policies
Ensure all modeled objects are documented using Snowflake comments and metadata standards
Validate modeled outputs for accuracy, completeness, and reconciliation to source-of-truth totals
Tune SQL for performance and cost efficiency, applying Snowflake best practices
Support SDLC and SOC 1 evidence requirements through clear documentation and controlled change processes
Support scheduled refreshes and dependencies using Snowflake-native orchestration or approved tooling
Monitor modeled layers and resolve data, logic, or performance issues affecting downstream consumers
Partner with upstream teams to triage and resolve source data issues when needed
Partner with BI developers to ensure semantic layers answer business questions without additional tool-side modeling
Collaborate with Data Engineers to align on grain, keys, freshness windows, and data contracts
Participate in sprint planning, estimation, and delivery ceremonies
communicate status and risks proactively
Use AI-assisted development tools to accelerate SQL development, refactoring, optimization, and documentation
Maintain full accountability for correctness, governance, and semantic integrity of all AI-assisted outputs
Requirements:
Strong hands on experience with Snowflake
Familiarity and comfortability with Data Vault 2.0 methodology
Advanced SQL expertise, including complex joins, window functions, and performance tuning
Strong understanding of dimensional modeling concepts including SCDs, surrogate keys, and conformed dimensions
Experience encoding business logic and KPI definitions into curated semantic views
Experience supporting semantic models and datasets consumed by Power BI and other BI tools
Familiarity with Snowflake governance features including data masking and sensitivity tagging
Experience supporting production data models through SDLC and incident resolution
Strong communication and documentation skills
Nice to have:
Experience optimizing Snowflake performance and cost
Experience optimizing datasets for Power BI performance and usability
Exposure to BI and AI consumption patterns requiring clear semantics and business-friendly metadata
Familiarity with Git-based workflows and Azure DevOps
Experience contributing to a centralized semantic or data modeling function
What we offer:
medical
dental
vision
work/life resources
retirement savings plans like 401(k)
paid days off such as parental leave and disability coverage