Business Intelligence Engineer

Maintain the BI Snowflake environment: schemas, roles and access, performance tuning, warehouse sizing, and cost monitoring 

Build and optimize Power BI semantic models and dashboards (star schemas, DAX measures, row-level security), tuning DirectQuery and Import models against Snowflake 

Translate business requirements, KPIs, and metric definitions into technical designs. 

Set engineering standards for naming, code review, CI/CD, and documentation 

Partner with the integrations team to define what the BI platform needs from landed data and resolve upstream issues 

Own data quality in the transformation layer: testing, monitoring, reconciliation, and root-cause analysis 

Write complex, performant SQL to model, transform, and validate data 

Develop and maintain dbt models (staging, intermediate, marts) with tests, documentation, and version control 

Set up and tune Snowflake Cortex Analyst and Semantic Views so natural-language questions return accurate answers 

Evaluate, test, and pilot AI-enabled analytics, such as natural language querying over semantic models, AI-generated commentary, and automated variance explanation. 

Mentor analysts and stakeholders on data models and self-service 

Pitch in with PowerBI report and dashboard builds when needed.

5+ years in data engineering, analytics engineering, or BI engineering 

Bachelor’s degree in Finance, IT, Information Systems, Engineering, or a related field (or equivalent experience) 

Advanced SQL and dbt, with a solid grasp of dimensional modeling and data warehousing concepts 

Deep hands-on Snowflake experience, including performance tuning, access control, and cost management 

Strong Power BI skills, including DAX, data modeling, and publishing and managing workspaces 

Git and CI/CD experience for analytics code 

Working fluency with AI tools in analytics or engineering work, with sound judgment about their limits, including inaccurate or ungrounded answers 

Clear communication with technical and non-technical audiences, and the ability to translate business needs into technical requirements and carry them through to deployment with strong attention to detail

Experience building ELT pipelines and data ingestion (Fivetran or similar, including API-based sources) 

Python for scripting and automation 

Experience with orchestration tools (Airflow, Dagster, or similar) 

Infrastructure-as-code experience (Terraform or similar) for Snowflake 

Hands-on experience with Snowflake Cortex Analyst, semantic views, or similar natural-language-to-SQL tools 

Familiarity with data governance, cataloging, and lineage tooling 

Background in finance, FP&A, or sales analytics 

Exposure to Tableau or other BI tools