Solutions Engineer

Role overview A boutique data and AI consultancy is hiring a Solutions Engineer who owns the full client lifecycle, from the first discovery call through production delivery. Unlike a typical pre-sales architect who hands the engagement off, this role requires the same person who scopes and prices the work to stand up the first sprint hands-on. The position carries a services revenue number and is central to a builder-led culture that values ownership and clear communication.

Responsibilities - Manage a book of new and existing accounts, carry a services revenue target against it, and forecast accurately - Lead technical discovery independently, mapping architecture without waiting for additional engineers - Scope and price engagements, including the risk-bearing parts of an estimate - Author proposals and statements of work, then defend scope against client pushback for additional scope at the same price - Stand up the first sprint on sold engagements with hands-on implementation before transitioning to a delivery lead - Build reference architectures, demos, and proofs of concept that mirror what will actually be deployed - Drive co-sell motions through partner channels at major data platform vendors - Feed field learnings back into service line definitions and pricing

Requirements - Five or more years in a client-facing technical role at a data platform, analytics, or AI company, such as Solutions Architect, Solutions Engineer, Sales Engineer, or technical Account Executive - Demonstrated revenue ownership, including a carried quota or named technical ownership of sizeable deals - Hands-on engineering within the last three years, with working SQL and Python, shipped pipelines using dbt, Airflow, or equivalent, and production debugging experience - Working depth on Snowflake or Databricks plus enough fluency in the other to participate in technical bake-offs - Credibility with both senior data leaders and staff engineers in the same room - Comfort selling scope and outcomes rather than licenses and features

Nice to have - Time inside a consultancy, systems integrator, or Big 4 practice with exposure to utilization and margin - Experience selling into private equity portfolio companies or value creation teams - Production work on RAG, agentic systems, or ML platforms that sustained real user traffic - An active partner network at Snowflake or Databricks

Benefits and work setup - Base salary plus variable compensation tied to deals sold and work that reaches production - An owned book of business, including a private equity channel and existing accounts with expansion runway - Engagement variety across industries and technology stacks rather than a single platform focus - Certification support across Snowflake, Databricks, AWS, GCP, and Azure - Remote-first working arrangement with client travel when an engagement requires it