Tech Stack

Description

You will join the Enterprise Data Platform Services team to build and operate self-service capabilities for data scientists and engineers, driving scalable data solutions for 84.51° and The Kroger Co. You'll collaborate with cross-functional teams to shape the future of the enterprise data platform, focusing on integration, observability, and AI-readiness.

Requirements

• Bachelor’s degree in computer science, Information Systems, Mathematics, or a related technical field (Master’s preferred)
• 5+ years of professional software development experience, including ownership of production systems
• Proficiency in Python and experience building APIs using FastAPI or comparable frameworks
• Strong SQL skills and experience working with structured data
• Experience with version control systems (Git, SVN)
• Deep familiarity with automated testing frameworks (e.g., Pytest, Unittest)
• Understanding of Agile methodologies (Scrum, XP, etc.)
• Knowledge of RESTful API design and integration
• Experience with performance tuning, debugging, and dependency management in production environments
• Understanding of CI/CD pipelines and object-oriented design principles (e.g., SOLID)
• Preferred: Experience with Microsoft Azure (Function Apps, Service Bus, AKS, etc.)
• Preferred: Experience with Databricks and PySpark
• Preferred: Familiarity with Domain-Driven Design and event-driven architecture
• Preferred: Exposure to platform engineering or self-service data tooling

Responsibilities

• Drive product development by collaborating with product managers and stakeholders to define, scope, and lead new feature development.
• Deliver platform-scale services using Python, FastAPI, Azure Kubernetes Service, and Databricks.
• Promote engineering excellence through well-tested, maintainable code and automated validation pipelines.
• Mentor and grow junior and mid-level engineers.
• Shape team workflows by leading retrospectives and technical design discussions.
• Partner with product managers to scope, estimate, and plan releases for mid- to large-scale initiatives.
• Collaborate across engineering teams to drive reusability and contribute to a modern data management platform.
• Elevate platform engineering practices and contribute to a culture of craftsmanship and operational excellence.
• Anticipate future needs for AI-readiness, including intelligent observability and autonomous agents.