As a Principal Engineer, you will shape the architecture, development, and evolution of critical enterprise software platforms that support digital transformation.
You will lead complex engineering initiatives from initial problem discovery and system design through implementation, deployment, and ongoing production support.
Working across application development, backend services, data engineering, and platform integrations, you will build secure, scalable, and reliable solutions that deliver measurable business value.
You will embrace AI-driven development practices, using advanced tools and coding agents to accelerate delivery while maintaining rigorous standards for quality, security, and correctness.
Collaborating with product, design, data, security, and infrastructure teams, you will turn ambiguous business challenges into practical technical solutions.
As a senior technical leader, you will influence architectural decisions, establish engineering best practices, and mentor engineers across the organization.
This remote opportunity offers the autonomy to drive meaningful technical change within an innovative, collaborative environment that values engineering excellence and continuous learning.
End-to-end engineering leadership: Lead complex software initiatives from understanding user needs and business objectives through architecture, implementation, deployment, production support, and continuous improvement.
Translate ambiguous requirements into actionable technical plans, balancing business priorities, engineering constraints, long-term maintainability, and delivery timelines.
Software architecture and development: Design, build, and evolve secure, scalable, and maintainable solutions across web applications, backend services, APIs, Model Context Protocol (MCP) servers and integrations, data pipelines, and platform services.
Contribute to application and service development using technologies such as Next.js, JavaScript, TypeScript, and Golang, while adapting to other appropriate languages and frameworks.
Develop and maintain data-driven systems involving dbt, Snowflake, PostgreSQL, relational databases, and enterprise integrations.
AI-driven software development: Integrate AI tools and coding agents throughout the software development lifecycle to accelerate research, technical exploration, implementation, refactoring, testing, documentation, and operational analysis.
Review, test, validate, and secure AI-assisted outputs, ensuring that all delivered software meets established standards for correctness, reliability, security, and performance.
Improve AI-driven development practices by refining tools, prompts, guardrails, and workflows that enhance team productivity without compromising engineering quality.
Technical quality and reliability: Lead architecture and code reviews, establish engineering standards, and promote best practices for secure development, automated testing, continuous integration, deployment automation, observability, and containerization.
Identify and address technical risks, including fragile integrations, poor data quality, unclear ownership, security vulnerabilities, operational blind spots, and accumulating technical debt.
Improve system reliability through effective debugging, root cause analysis, performance optimization, and continuous production monitoring.
Data engineering and integrity: Design and improve database schemas, data pipelines, migrations, and queries to ensure data quality, integrity, performance, and consistency across connected systems.
Establish appropriate validation practices and controls to protect critical data and support dependable business reporting and operational processes.
Cross-functional collaboration: Partner with Product, Design, Data, Security, Infrastructure, and business stakeholders to define requirements, evaluate trade-offs, and deliver solutions aligned with organizational objectives.
Communicate technical decisions, architectural considerations, risks, and implementation plans clearly to both technical and non-technical audiences.
Technical strategy and mentorship: Shape engineering roadmaps, influence platform strategy, and contribute to decisions that extend beyond individual projects or teams.
Mentor engineers, share technical knowledge, encourage continuous learning, and help establish a culture of ownership, accountability, collaboration, and engineering excellence.
Identify opportunities to simplify systems, improve development efficiency, and introduce technologies and practices that create sustainable long-term value.