Solutions Architect

About the role

Solutions Architects drive customer adoption of LaunchDarkly within AI initiatives. They act as trusted advisors to enterprise clients, aligning technical goals with feature management best practices. This role focuses on enabling safe, observable, and experimental AI delivery for modern software teams.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

Requirements

Write production-quality code in one or more modern languages such as Python, Java, JavaScript/Node.js, or Go, and independently build working integrations and prototypes in customer repositories.

Possess hands-on experience with AI-assisted development tools like Claude Code or Cursor.

Understand Model Context Protocol (MCP) or similar agent-based tooling, or demonstrate a strong interest in learning these approaches.

Guide customers on DevOps, CI/CD, and modern release practices, including experimentation and progressive delivery.

Deliver across the full software development lifecycle in enterprise environments and lead teams through development or platform transformations.

Comprehend how feature management and experimentation platforms reduce delivery risk and accelerate software delivery.

Operate confidently with major cloud providers, Linux, and containerized environments.

Be open to 20% travel for client engagements.

Practical notes

This role operates under high trust and transparency, with compensation ranges published for geographic zones.

Open roles are available to U.S. candidates, and equal opportunity principles are upheld throughout the hiring process.

Accommodations for disabilities can be requested through the official apply page.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Solutions Architects combine consulting expertise with hands-on technical implementation in customer environments.

The role focuses on AI feature management, progressive delivery, and experimentation in production systems.

Proficiency in modern feature flags, observability, and release practices is central to success.

Collaboration with engineering, platform, and product teams shapes how reference implementations evolve.

Fluency in both technical and business communication is essential when working with enterprise stakeholders.

Hands-on coding is a core part of the role, not just presentation or deck creation.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.