Technical Advisor

About the role

This Technical Advisor role delivers focused technical advisory to the Head of Engineering so scaling methods keep pace with company growth and evolving product demands. The hire owns the design and validation of engineering scaling strategies across the full product lifecycle, ensuring alignment between technical execution and business objectives. You will act as a hands on strategic partner, translating complex technical constraints into actionable roadmaps for product and engineering teams in the United States. The role requires independent judgment to diagnose bottlenecks, propose corrective actions, and challenge existing assumptions about architecture and team structure. You will own the narrative behind technical tradeoffs, helping leadership communicate rationale to stakeholders at every level of the organization. This engagement is part time, which means you must prioritize impact over activity and deliver clear, measurable outcomes with minimal ongoing supervision. Success in this position is defined by your ability to drive sustainable scaling practices that remain effective as the company continues to grow.

Key facts

What you'll do

Establish clear scaling objectives in partnership with the Head of Engineering and validate them against real time product metrics and team performance data.

Diagnose current engineering bottlenecks by analyzing workflows, tooling, and communication patterns across product and technology teams in the United States.

Design practical interventions that align team structures, processes, and technical practices with the pace of company growth and product complexity.

Build and maintain a living model of engineering capacity, outlining scenarios that account for variations in staffing, technical debt, and delivery commitments.

Champion the use of data informed decision making by embedding analytics into planning rituals, retrospectives, and strategic discussions with cross functional stakeholders.

Evaluate emerging tools, frameworks, and methodologies for DevOps, data science, and full stack development, translating their potential impact into recommendations for adoption.

Collaborate closely with data analysts, data scientists, and data engineers to ensure that operational data reliably informs scaling strategies and resource allocation.

Mentor internal product and engineering leads on best practices for team building, technical standards, and sustainable delivery under rapid growth conditions.

Champion the creation of robust engineering processes around technically complex products, ensuring that quality, reliability, and scalability remain central priorities.

Translate advisory work into concise artifacts, such as diagrams, process maps, and performance summaries, to make recommendations accessible to diverse audiences.

Establish feedback loops that capture the outcomes of implemented changes, enabling continuous refinement of scaling methods and long term roadmap decisions.

Represent the perspective of scaling engineering teams in high level discussions, balancing technical feasibility with timelines, risks, and business priorities.

Requirements

A bachelor's degree is required, and the exact field must be confirmed Hands on experience as a CTO at early stage VC backed tech startups is mandatory to understand startup dynamics and the pressures of rapid scaling.

Demonstrated success scaling engineering teams from roughly zero to 50+ engineers within the last few years is mandatory to prove you can handle growth related complexity.

Evaluation covers AI, DevOps, Data Science, Full stack, and other technical roles for comprehensive assessment of your cross domain expertise.

Building engineering teams around a technically complex product is expected, requiring deep familiarity with architecture decisions and delivery challenges.

Physical presence in the USA is required because the advisory work happens on site and cannot be replicated remotely.

The engagement is part time, which means you must manage multiple priorities and deliver high impact work within limited hours.

Practical notes

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

Continuous learning supports professionals who translate numbers into decisions and keeps your advisory skills relevant in a fast moving technical environment.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

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 and secure influence in strategic discussions about engineering scaling and long term product direction.