Sr. Platform Engineer

Drive product development: Collaborate with product managers and stakeholders to define, scope, and lead new feature development that meets evolving user needs and aligns with enterprise priorities. Deliver platform-scale services: Contribute to the implementation and operation of core data platform capabilities using technologies such as Python, FastAPI, Azure Kubernetes Service (AKS), and Databricks. Promote engineering excellence: Lead by example in writing well-tested, maintainable code. Champion unit and integration testing and contribute to automated validation pipelines. Mentor and grow others: Provide coaching, feedback, and technical guidance to junior and mid-level engineers, fostering a culture of learning and continuous improvement. Shape how we work: Lead retrospectives and technical design discussions, identifying opportunities to improve delivery pipelines, team workflows, and system reliability. Drive estimation and execution: Partner with product managers to scope, estimate, and plan releases for mid- to large-scale initiatives, balancing technical constraints with business value. Innovate with peers: Collaborate across engineering teams to bring new perspectives to shared problems, drive reusability, and contribute to a modern, flexible data management platform. Elevate platform engineering: Evolve engineering best practices across the team and platform, contributing to a culture of craftsmanship, experimentation, and operational excellence. Anticipate future needs: Contribute to the evolution of our platform as we prepare for AI-readiness, including foundational support for intelligent observability and autonomous agents.

10+ years of professional software development experience, including ownership of production systems Experience working on a Platform as a Data Engineer or Software Engineer Proficiency in Python and experience building APIs using FastAPI or comparable frameworks Strong SQL skills and experience working with structured data Experience working in cloud environments, especially Microsoft Azure (Function Apps, Service Bus, AKS, etc.) Experience with Databricks and PySpark for data-intensive applications Experience with Domain-Driven Design and event-driven architecture Experience with version control systems (Git, SVN) Deep familiarity with automated testing frameworks (e.g., Pytest, Unittest) Databricks certified Data Engineer Professional Cloud provider certifications, such as Azure Certified Solutions Architect