Category Management Data Engineer

The Bluebird Group•Minneapolis, MN

2d•Onsite

About The Position

We’re looking for a resourceful, ambitious builder excited to work at the intersection of retail analytics, data engineering, and technology enablement. This entry-level role (1–2 years of software or data engineering experience) is ideal for someone who loves data, enjoys solving problems, and wants to help create the next generation of retail capabilities. Foundational knowledge of software and data engineering is important, plus the grit to learn fast, navigate roadblocks, and take ownership of meaningful projects from day one. You’ll work closely with our Category Management and Tech teams to deliver thoughtful reporting and insights while also helping automate workflows, build data pipelines, and support new software tools.

Requirements

Azure infrastructure familiarity (or equivalent experience with AWS/GCP and ability to adapt).
Strong SQL skills and understanding of relational data modeling.
Python for data engineering (ingestion, transformation, modeling).
Experience building and orchestrating data pipelines, including REST/GraphQL API integration.
0–2 years in software engineering or data engineering.
Understand databases, data structures, and data cleaning/transformation.
Experience with SQL, Python, and BI tools.
Strong analytical mindset, attention to detail, and clear communication skills.

Nice To Haves

Familiarity with Next.js / Node.js / static web apps (useful for internal tool extensions).
Power BI experience for dashboards and analytical visualizations.
Familiarity with Git for version control and Agile methodology

Responsibilities

Partner with Category Management leadership to define priorities and product roadmap.
Run weekly/monthly reporting across syndicated, POS, retail, digital, and social datasets.
Build actionable insights and clear, best-in-class presentations for internal and client needs.
Deliver ad hoc reporting as business needs evolve.
Automate reporting by integrating multiple data sources and building efficient pipelines.
Build, maintain, and troubleshoot data pipelines using company-standard tools and cloud architecture.
Serve as backend data owner for new internal software — validating data quality, defining logic flows, and supporting functional requirements.
Diagnose data issues, validate outputs, and maintain consistency across reporting layers.
Document processes, data models, and logic to create a sustainable, maintainable ecosystem.
Take on increasingly complex projects as technical depth grows.