Data Engineer - Supply Chain Analytics & Data Enablement

The Aldo Group Inc / Le Groupe Aldo Inc.•Montreal, QC

21h

About The Position

The ALDO Group is seeking a Data Engineer to join the Supply Chain organization, embedded within the broader Data & Analytics ecosystem. This role is responsible for designing, developing, and maintaining data solutions that bring external and cross-functional data sources into the organization’s analytics platform, and for supporting the migration of existing reporting from Power BI to Amazon QuickSight. Reporting to the Chief Supply Chain Officer with a dotted line to the VP Data, AI & Development, this position bridges supply chain business needs with enterprise data engineering standards. The Data Engineer will work within the established AWS-native data stack, collaborate closely with the central data engineering team, and ensure that supply chain data products are reliable, well-governed, and aligned with organizational architecture. The ideal candidate brings hands-on experience building production-grade data pipelines on AWS, comfort working with diverse and sometimes messy external data sources (Excel files, flat files, PDFs, direct integrations, and potentially EDI feeds), and a practical understanding of BI tooling. Supply chain domain experience and a working knowledge of data science concepts are valued assets but not strict requirements.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, or a related field
3 or more years of hands-on experience in data engineering, with a strong focus on building and operating production-grade data pipelines
Experience designing, building, and maintaining data solutions using AWS technologies including Athena, AWS Glue, S3, and related services
Strong proficiency in Python and SQL for data transformation, orchestration, and automation
Practical experience ingesting and normalizing data from diverse external sources including Excel/CSV files, PDFs, APIs, and file-based integrations
Working knowledge of BI platforms, with hands-on experience in Power BI, Amazon QuickSight, or both
Solid understanding of data modeling, data architecture, and lakehouse patterns for analytics platforms
Good communication skills and the ability to work effectively with both technical teams and non-technical business stakeholders

Nice To Haves

experience with Redshift, dbt, Apache Hudi, and Lambda is a strong asset
experience migrating between BI tools is a strong asset
Supply chain domain experience and a working knowledge of data science concepts are valued assets
Experience contributing to platform standards, documentation, and operational processes is a strong asset

Responsibilities

Design, develop, and maintain data ingestion pipelines that bring external data sources (Excel, CSV, PDF, direct integrations, and potentially EDI) into Amazon Athena and the broader AWS data lake
Lead the migration of existing Power BI dashboards and reports to Amazon QuickSight, including data layer preparation, semantic modeling, and validation of outputs against source-of-truth data
Partner with supply chain stakeholders to understand reporting and analytics requirements, define data contracts, and deliver reliable datasets that support operational and strategic decision-making
Build and maintain scalable data models for supply chain analytics, ensuring consistency across raw, curated, and consumption layers within the enterprise data architecture
Work within the AWS technology stack including Athena, AWS Glue, S3, Redshift, dbt, Apache Hudi, and Lambda to build and deploy robust data workflows aligned with enterprise standards
Implement and maintain data quality checks, validation rules, and observability to ensure trusted data for reporting, dashboards, and downstream applications
Troubleshoot and resolve data processing, data quality, and production issues end to end, including root cause analysis and permanent fixes
Collaborate with the central data engineering team to align on platform standards, code review practices, and architectural patterns
Maintain clear documentation for pipelines, data models, and operational runbooks to support maintainability and onboarding
Stay current with industry best practices in data engineering, analytics engineering, BI tooling, and supply chain data enablement