Power BI Analyst (Data Modeling + Visualization)

📍LATAM - 100% Remote

🤝Contract - USD

Role Summary

🗣️Advanced English skills is required

Architect and maintain certified semantic datasets and dashboards in Power BI and Sigma. Serve as a “BI engineer” who ensures that reports are accurate, fast, and trustworthy by design.

Key Responsibilities

• Semantic Data Models: Design and implement data models (star schemas, dimension tables,
  
  

fact tables) for BI. Create version-controlled datasets (in Power BI or Sigma) with standardized

KPI definitions, row-level security, and clear lineage. Ensure each model is documented (schema

diagrams, data dictionaries) and reusable by analysts.

• Dataset Quality Gates: Develop automated pre-publish validations for datasets: check row
  
  

counts, data freshness, and parity of calculated metrics against source references. Surface

validation statuses on dashboards (e.g. “data verified” badges) to reassure consumers.

Implement CI/CD for BI artifacts (e.g. Git-driven deployment of PBIX or Sigma assets).

• Dashboard Development: Build executive scorecards and dashboards (audience engagement,
  
  

revenue/attendance, operational metrics). Embed telemetry to track report usage and

performance. Periodically prune or optimize dashboards to remove redundancy and improve

query efficiency.

• Expose QA Insights: Incorporate data quality test results (e.g. pass/fail indicators from Data
  
  

Quality Engineering) into visual reports. Partner with data engineering and DQE teams to align

data contracts and measurement tests with BI reports.

• Training & Governance: Train analysts on model best practices (efficient queries, modular
  
  

design). Create guidelines for when to extend existing models vs. build new ones. Document all

models, dashboards, and processes in Confluence or via a BI portal.

Core Technical Skills

• Power BI: Advanced skills in Power BI Desktop (Power Query/M, DAX) and Power BI Service.
  
  

Ability to implement incremental refresh, aggregations, and optimization techniques for large

datasets.

• Sigma Computing: Experience with building and securing Sigma data models (joins, parameters,
  
  

row-level security) and writing efficient SQL behind the scenes.

• SQL & ETL: Strong SQL (Snowflake or similar) for transforming and loading data. Familiarity with
  
  

data transformation tools (dbt, Azure Data Factory).

• Validation Automation: Ability to script or schedule dataset validation checks (e.g. via Python or
  
  

SQL jobs). Understanding of CI/CD pipelines for BI (Azure DevOps, GitHub Actions).

• Analytics Tools: Basic knowledge of web analytics (GTM, GA4) is helpful for tracing metric
  
  

origins. Understanding of UTM parameters and attribution is a plus.

• Tools & Collaboration: Use Jira/ServiceNow for BI project tracking and issue management.
  
  

Maintain detailed documentation (Scribe, Confluence) for data models and report usage.

Nice-to-Have

• Experience with other BI platforms (Tableau, Looker) or semantic layers (Power BI Analysis
  
  

Services).

• Knowledge of advanced data modeling (Data Vault, Kimball methodology).
• Familiarity with SQL database tuning and indexing to improve query speed.
• Skills in UX design for dashboards (ensuring visual best practices and accessibility).