Data Analyst

Westmount, QC
On-site
Posted Oct 27, 2026
1 position

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Employment type: Full-time
Experience level: Entry, Junior · 0+ years
Posting language: English
Working hours: 40 hours per week
Seniority: Not Applicable
Application method: Apply on LinkedIn (LinkedIn sign-in required)

Job summary

Validate and reconcile property and ERP migration data, investigate discrepancies, and produce recurring leadership KPI reports using SQL and BigQuery. Analyze business questions for senior leaders and help develop scalable data models that reduce manual reporting.

Job details

Data Analyst, Analytics & Reporting Gestion Zagora | Montréal, QC | On-site | Full-time The opportunity Gestion Zagora manages more than 8,000 residential units across Canada and the US. The Data Strategy & Innovation team partners with Finance, Asset Management, Operations, Accounting, and Leasing to replace manual reporting with automated, verified numbers. You report directly to the Head of Data Strategy & Innovation and you'll be the person leadership comes to when they need to know what the data actually says. What you'll actually do Data validation. Verify that what's in our warehouse matches reality. Rent rolls, unit counts, lease terms, collections, vacancy. You'll build reconciliation checks, chase down variances with the operating teams, and document what you find. This is a large share of the job and it is the foundation everything else rests on. ERP migration support. You'll validate migrated data set against set, investigate mismatches, and sign off on what's clean. This is the fastest way anyone has ever learned how a property management business works. Reporting. Help the production of recurring KPI reporting: weekly and monthly packages for leadership, bilingual French and English, built on SQL against BigQuery rather than copy-paste. Where a report is still manual, your job is to make it not be. Analytics. Answer real questions from senior leadership. Why is turnover up in one portfolio, what does our collections curve look like by region, which buildings are underperforming their market rent and by how much. You'll define the metric, pull the data, and present the answer. Data modelling input. Help translate operational questions into structures that scale, so the same question next quarter takes ten minutes instead of two days. What we're looking for Strong SQL. Joins, aggregations, window functions, and the habit of validating your own output before anyone else sees it Advanced Excel or Google Sheets. Pivot tables, lookups, and the judgment to know when a spreadsheet is the wrong tool Comfort with large, messy, inconsistent datasets and the patience to reconcile them line by line Clear written and spoken French and English. Our reports, stakeholders, and source data are all bilingual Ability to explain a number to leadership and defend how you got it Bonus: Python and pandas, BigQuery, Power BI, Apps Script, real estate or property management data, accounting or finance coursework, ERP exposure of any kind Background in finance, accounting, economics, engineering, or anything quantitative works. The right mindset You are bothered when two reports disagree, and you don't stop until you know which one is wrong Naturally curious. You dig into problems rather than waiting for direction Structured and independent. Small team, no micromanagement You want to grow toward senior analyst or analytics engineering, and you're happy to pick up Python and warehouse modelling along the way How you'll grow We have a defined career ladder for this team. This role is the first rung on the analytics track, with a path toward owning a reporting domain and eventually the business-facing side of the department. We run bi-weekly internal technical sessions, and our head of department is a former quantitative developer who trains actively.

What you’ll do

Requirements

The role requires strong SQL, advanced Excel or Google Sheets skills, comfort working with large and inconsistent datasets, and clear written and spoken French and English. Candidates must be able to explain and defend their analysis; Python, BigQuery, Power BI, Apps Script, ERP exposure, and relevant quantitative or finance backgrounds are bonuses.