Logistics Data Analyst — Actionable Insights

Bison Transport Inc; Winnipeg; On-site; CAD 60,000 - 90,000; Full time

Benefits offered by this job

Wellness Program

Extracurricular sports teams

Corporate social responsibility (CSR)

Job summary

Bison Transport Inc is seeking a Data Analyst to translate ambiguous business questions into scoped analyses and data-backed answers. You will work across Asset, Logistics, Intermodal and LTL, using SQL, Python and Power BI to deliver actionable insights for operations, commercial, and leadership teams.

The role emphasizes quick learning of different business contexts, strong data governance, and clear communication of findings to a non-technical audience.

Qualifications

• 2–5 years in an analytics or data role producing decision‑grade work.
• Strong SQL, including multi‑table joins and data validation.
• Working Python for data analysis, including scripting investigations.
• Sufficient Power BI to build and maintain a clean, accurate view.
• Analytical judgment and ability to explain technical work to non‑technical audiences.

Responsibilities

• Translate open-ended questions into scoped, answerable analyses.
• Write and validate SQL against a governed data warehouse to pull data.
• Use Python for analysis, transformation, and investigations beyond SQL.
• Build focused Power BI views to present visuals as answers.
• Support multiple lines of business (Asset, Logistics, Intermodal, LTL) as priorities shift.
• Surface data-quality issues and state assumptions and caveats clearly.

Skills

SQL

Python

Power BI

Data analysis

Analytical thinking

Communication

Tools

SQL Editor

Python (Pandas)

Power BI

Job description

Bison Transport Inc is seeking a Data Analyst to translate ambiguous business questions into scoped analyses and data-backed answers. You will work across Asset, Logistics, Intermodal and LTL, using SQL, Python and Power BI to deliver actionable insights for operations, commercial, and leadership teams.

The role emphasizes quick learning of different business contexts, strong data governance, and clear communication of findings to a non-technical audience.