Job Description 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.
Key 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
Qualifications - 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; experience with Redshift, dbt, Apache Hudi, and Lambda is a strong asset
- 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; experience migrating between BI tools is a strong asset
- 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
- Experience contributing to platform standards, documentation, and operational processes is a strong asset
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