Principal Software Engineer - Full Stack

Premier Inc.

2d•$141,000 - $234,000•Remote

About The Position

The Principal Software Engineer (Full Stack) is a senior individual contributor who remains deeply hands-on while elevating the technical quality of full-stack development across teams and product domains. This role writes and reviews production code on the most complex and ambiguous problems, leads architecture decisions through direct contribution rather than delegation, and raises the engineering bar through mentorship grounded in active delivery. The Principal Engineer is not a manager – they are Premier’s most technically capable full-stack practitioner, and their impact is measured by the quality and scalability of what they build and enable. In addition to traditional full-stack development, this role will operate across modern data and AI platforms, including environments such as Foundry and Databricks, where engineers design and build data-driven and intelligent applications. The Principal Engineer is comfortable working at the intersection of application development, data platforms, and emerging AI capabilities—leveraging large-scale data pipelines, analytics infrastructure, and AI/ML services to power product features and decision-support systems. This includes collaborating on architectures that integrate operational software systems with data platforms and AI-enabled services to deliver scalable, insight-driven, and automation-enabled solutions.

Requirements

Years of Applicable Experience - 10 or more years
Bachelors (Required)
Expert-level full-stack engineering: production delivery across distributed systems, API design (REST, GraphQL, event-driven), and modern frontend frameworks (React, TypeScript)
Cloud platform architecture on Azure, AWS, and/or GCP at enterprise scale
Infrastructure-as-code (Terraform) and DevSecOps practices
Hands-on technical leadership: driving quality through working code, ADR authorship backed by implementation, and design reviews grounded in direct contribution
Observability and reliability engineering: Grafana, DORA metrics, performance debugging, and production incident ownership
Remain in a stationary position for prolonged periods of time
Be adaptive and change priorities quickly; meet deadlines
Attention to detail
Operate computer programs and software
Ability to communicate effectively with audiences in person and in electronic formats.
Day-to-day contact with others (co-workers and/or the public)
Making independent decisions
Ability to work in a collaborative business environment in close quarters with peers and varying interruptions

Nice To Haves

Advanced cloud certifications (Azure Solutions Architect, GCP Professional) strongly preferred
Palantir Foundry, Databricks, or equivalent data platform integration experience
Kafka or event streaming architecture
Proficiency with AI-assisted development tools (Claude Code, GitHub Copilot Enterprise) as a force-multiplier for individual engineering output
Security architecture and CIAM integration patterns
Full-stack production delivery; distributed systems implementation; API strategy and hands-on development (REST, GraphQL, event-driven); Terraform & cloud infrastructure
Advanced design patterns implemented in production systems; system-wide performance tuning and reliability engineering; security-by-design in working codebases
Enterprise cloud platforms (Azure, AWS, GCP); Kafka or event streaming; Grafana observability; AI-assisted SDLC tooling (GitHub Copilot, Claude Code)

Responsibilities

Design and implement full-stack architectural patterns through direct, hands-on contribution.
Author architecture decision records (ADRs) grounded in working code, lead design reviews with active prototyping, and personally build reference implementations that teams can adopt.
Own the hardest architectural problems – those requiring both deep system thinking and the ability to ship.
Contribute production-quality code to the most complex features, platform capabilities, and performance-critical systems across the stack.
Build and maintain shared libraries, API patterns, and observability tooling (Grafana, DORA metrics).
Actively use and champion AI-assisted development tooling (Claude Code, GitHub Copilot Enterprise) to accelerate individual and pod-level output.
Evaluate emerging technologies through working proof-of-concepts, not just review.
Mentor senior and mid-level engineers through pair programming, hands-on code review, and collaborative design sessions – not through directives.
Partner with engineering managers on hiring bar calibration and DORA metric improvement by modeling the behaviors and output expected at each level.
Influence org-wide engineering quality through the code and systems built, not through committee participation.