Senior Solutions Architect
Job description
About the role
In this role focused on AI delivery, you partner with the Revenue team to guide transformative implementations and act as a trusted advisor throughout customer journeys. You will translate complex platform capabilities into clear, business-focused narratives that align feature management and experimentation with measurable outcomes. This position requires you to own the consultative discovery process, framing technical solutions against strategic priorities for each engagement. You will design and articulate the architecture needed to support AI-assisted workflows and progressive delivery in production environments. Your work will directly influence how customers adopt and scale their release and experimentation practices. You will act as a bridge between product vision and implementation reality for enterprise clients. Success in this role is defined by your ability to drive adoption through credible, hands-on guidance. You will represent the Services organization and Revenue team in high-stakes discussions that shape long-term platform strategy.
Key facts
What you'll do
Demonstrate expert-level knowledge of feature management, experimentation, and release practices within enterprise contexts.
Write production-quality code in one or more modern languages such as Python, Java, JavaScript/Node.js, or Go to build integrations and prototypes directly in customer repositories.
Use AI-assisted development tools like Claude Code or Cursor and apply principles of the Model Context Protocol (MCP) or similar agent-based approaches in solution design.
Guide customers through DevOps, CI/CD, and modern release practices, including experimentation, progressive delivery, and risk mitigation.
Lead full lifecycle delivery across complex enterprise environments and drive platform transformation initiatives with cross-functional teams.
Explain how feature management and experimentation platforms accelerate delivery while reducing risk across the software development lifecycle.
Operate effectively within Linux and containerized environments and collaborate closely with major cloud providers such as AWS, Azure, or GCP.
Conduct discovery sessions, design workshops, and architecture reviews to define solution approaches that meet strict enterprise requirements.
Accept occasional travel, up to 20%, to support on-site customer engagements and stakeholder meetings.
Build compelling business cases and return-on-investment narratives that justify platform adoption and expansion.
Partner with product and engineering teams to translate customer feedback into actionable platform enhancements.
Maintain strict attention to security, compliance, and operational best practices in every implementation you support.
Serve as a hands-on technical advisor who balances strategic vision with the realities of existing codebases and deployment constraints.
Document methodologies, playbooks, and decision frameworks to ensure consistency and repeatability across customer engagements.
Champion the use of data and experimentation to validate hypotheses and de-risk major platform adoption decisions.
Requirements
Bring 4+ years of experience in enterprise software, platform engineering, or solutions architecture with a hands-on, consultative approach to solving real business problems.
Write production-quality code in one or more modern languages such as Python, Java, JavaScript/Node.js, or Go, and independently build and ship working integrations and prototypes directly in customer repositories.
Use AI-assisted development tools like Claude Code or Cursor and understand Model Context Protocol (MCP) or similar agent-based approaches to designing scalable, reliable systems.
Guide customers in DevOps, CI/CD, and modern release practices, including experimentation, progressive delivery, and the management of feature flags and rollouts.
Deliver across the full software development lifecycle in enterprise environments and lead teams through development or platform transformations with measurable outcomes.
Explain how feature management and experimentation platforms accelerate and de-risk software delivery while aligning technology investments with business objectives.
Work with major cloud providers such as AWS, Azure, or GCP, and operate comfortably in Linux and containerized environments, including orchestration with Kubernetes and related tooling.
Accept occasional travel, up to 20%, to support customer engagements that may require on-site presence and collaboration.
Demonstrate strong written and verbal communication skills, with the ability to simplify complex technical concepts for diverse stakeholders.
Show evidence of owning end-to-end solutions in past roles, from discovery and design through implementation, validation, and post-launch support.
Approach problem-solving with structured thinking, combining data, user context, and operational constraints to recommend robust solutions.
Commit to working collaboratively in a high-trust, high-transparency environment focused on enterprise software delivery and AI experimentation.
Maintain professionalism and reliability in meeting commitments to customers and internal partners under tight timelines.
Practical notes
This role is based in a Remote
US location and involves occasional travel.
The team operates with high trust and transparency, focusing on enterprise software delivery and AI experimentation.
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
This role centers on enterprise software delivery and AI experimentation using feature management and progressive release techniques.
You will work directly with modern tooling for feature flags, experimentation, and agentic workflows.
The position requires both advisory consulting and hands-on implementation in customer environments.
Collaboration with product and engineering teams helps influence platform capabilities based on real customer needs.
Success depends on strong communication across technical stakeholders and the ability to dive into codebases quickly.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
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.