Solution Engineer

Role overview

This role embeds directly with enterprise customers to translate high-stakes business decisions into working decision intelligence systems that run against the customer's own data inside their cloud account. The position owns outcomes end to end, from discovery and modeling through implementation, performance tuning, and production hardening, while feeding platform improvements back into the core product. It suits builders who thrive in autonomous, high-friction environments and treat every engagement as both a delivery commitment and a contribution to the roadmap.

Responsibilities

- Run technical discovery with executives, domain experts, and data teams to surface the decisions that materially move the business, from misplaced inventory and hidden risk concentrations to fraud patterns and capacity guesses. - Model customer domains in a semantic ontology, formulate reasoning problems, and ship solutions that execute against live data inside the customer's cloud environment. - Design and build decision applications combining rules, graph analytics, optimization formulations, and predictive models on the platform's modeling, reasoning, and learning stack. - Identify gaps in the platform, scope and build workarounds, and push general-purpose versions upstream through code contributions rather than tickets. - Convert one-off solutions into reusable reference implementations, document as you go, and refuse shortcuts that compound into technical debt. - Translate deployment signal into product input by bringing back concrete reproductions, patterns, and specific failure modes.

Requirements

- Five or more years building and shipping production software, with meaningful time spent inside customer or partner environments. - Strong SQL plus deep familiarity with cloud data platforms such as Snowflake, BigQuery, Databricks, or Redshift. - Strong programming ability in Python, with comfort in declarative or logic-style languages considered a real advantage. - Demonstrated end-to-end ownership of an ambiguous problem taken from statement to running production, including the parts that went wrong. - Ability to read unfamiliar source code, interpret stack traces, and debug systems you did not write. - Comfort operating in high-autonomy, low-instruction settings and holding technical conversations with both a VP and a staff engineer in the same room.

Nice to have

- Built analytical, decision, or reasoning applications that reached production and remained load-bearing. - Experience with optimization, constraint solving, rule engines, graph algorithms, or machine learning on structured data. - Semantic modeling, data pipelines, and governance experience inside real enterprise settings. - A track record of upstream contributions: features, tools, or abstractions built for one customer that became standard for everyone. - Prior experience in enterprise technology, AI, or analytics platforms.

Benefits and work setup

- Remote-first role with global hiring. - Base salary range of $170,000 to $200,000 plus equity and benefits, with actual compensation varying by experience and location. - Open PTO, flexible schedules, and scheduled recharge weeks. - Mental-health support and learning stipends. - Regular team offsites and global events for a distributed workforce. - Transparent culture with open communication through standups, fireside chats, and shared meetings.