Analytics Engineer

Wirelessdna•Toronto, ON

61d•$75,000 - $85,000•Onsite

About The Position

This is not a “maintain the status quo” hire. We have a real mandate to modernize how data is managed and delivered across the business, leadership that supports it, and the infrastructure investment already underway. If you are someone who gets energized by owning data end-to-end — from the pipeline that builds it to the dashboard that tells the story — this role was built for you.

Requirements

3–5 years of experience in a data analyst, BI engineer, analytics engineer, or similar hands-on data role.
Demonstrated experience building automated data processes that replaced manual work — not just improved it.
Strong SQL: complex queries, joins, aggregations, CTEs, and transformation logic.
PySpark / Spark SQL: notebook-based data processing and transformation.
Advanced Power BI: report development, data modelling, DAX measures, and performance optimization.
Power Query (M language): custom transformations and query optimization.
Advanced Excel: pivot tables, dynamic arrays, Power Query integration.
A genuine curiosity about AI tools and where they can be applied — you follow this space, you experiment, you have opinions.
Self-directed: you identify problems without being told and you drive solutions to completion.

Nice To Haves

Hands-on experience with Microsoft Fabric: Lakehouses, Dataflow Gen2, Fabric Pipelines, OneLake.
Experience using LLMs or ML tools in a practical data or automation context (even prototyping counts).
Familiarity with agentic AI frameworks or AI-assisted workflow design.
Python (pandas, data wrangling) as a complement to PySpark workflows.
Exposure to telecom, dealer, or commission-based business models.
Experience building or maintaining medallion (bronze/silver/gold) data architectures.
Familiarity with Dataverse / Dynamics 365 data structures.
Background working in lean IT teams where you own the full stack from raw data to report.

Responsibilities

Data Engineering & Pipeline Development
Design and maintain data pipelines using PySpark and SQL, supporting a medallion architecture (bronze → silver → gold).
Build ingestion, transformation, and validation workflows that are clean, documented, and maintainable by others.
Apply schema validation, deduplication logic, and data integrity checks as standard practice.
Use Microsoft Fabric (Lakehouses, Dataflow Gen2, Fabric Pipelines) as the primary platform.
Reporting & Analytics
Build and maintain recurring business reports — sales performance, churn analysis, revenue variance, and commission reconciliation.
Develop and publish Power BI dashboards with DirectLake or import-mode datasets, using DAX for calculated measures and KPIs.
Translate business questions from stakeholders into accurate, well-structured data outputs.
Proactively surface anomalies and data quality issues.
Process Automation
Identify manual, repetitive reporting processes and deliver automated replacements.
Use Power Query, Dataflow Gen2, and notebook-based pipelines to streamline data preparation.
Document data models, transformation logic, and report definitions to an audit-grade standard.
Collaboration & Delivery
Work closely with business stakeholders to understand reporting requirements and validate outputs.
Scope clearly, execute promptly, and communicate blockers early.
Document data models, transformation logic, and report definitions to an audit-grade standard.
Maintain organized, version-controlled artifacts in SharePoint.