Data Analyst (Hybrid) JP922

Pathlion; Edmonton; On-site; CAD 60,000 - 90,000; Full time

Job summary

Pathlion is seeking a Data Analyst to join our team in Edmonton, Canada. You will contribute during the model design and build phases by validating data models, canonical definitions, and Unified Business Object designs using environmental monitoring and regulatory data to ensure data foundations reflect business operations.

You will perform semantic analysis on core data layers, identify safe standardization boundaries, and surface edge cases early.

Responsibilities

• Model Validation: Test proposed canonical elements against actual datasets and validate Unified Business Object model assumptions using historical monitoring data, reporting templates, and program datasets.
• Semantic analysis at core data layers: Analyze measurement comparability, unit normalization issues, data type and temporal consistency (sample dates vs reporting dates).
• Identify safe vs potentially breaking standardizations and surface edge cases early during design and build phases.
• Analytical Prototyping in the Data Management Platform: Build exploratory views to test aggregation behavior, trend stability, and query feasibility at scale for large ambient monitoring datasets.

Job description

Working alongside existing programs and technical staff, the Data Analyst provides focused analytical capacity during the model design and build phases. This includes validating and testing core data models, canonical definitions, and Unified Business Object designs using real environmental monitoring and regulatory data to ensure the data foundation reflects how the business operates.

Responsibilities:

• Model Validation
  • Test proposed canonical elements against actual datasets.
  • Validate Unified Business Object model assumptions using:
    • Historical monitoring data
    • Reporting templates
    • Program specific datasets
  • Surface edge cases early during design and build phases.
• Semantic analysis at the core data layers
  • Analyze:
    • Measurement comparability
    • Unit normalization issues
    • Data type and temporal consistency (e.g., sample dates vs reporting dates)
  • Identify where:
    • Standardization is safe vs. standardization would break meaning
  • Produce evidence-based recommendations to support the data architecture.
• Analytical Prototyping in the Data Management Platform
  • Build exploratory analytical views to test:
    • Aggregation behavior
    • Trend stability
    • Query feasibility at scale, especially for large, ambient monitoring datasets
  • Answer questions like:
    • Can this model support cross program trend analysis?
    • Does the chosen data model work at scale?