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LeanTaaS is a growth-stage AI and analytics company transforming hospital and infusion center operations. Over 190 health systems and 1,200+ hospitals across 49 states use their award-winning products to increase patient access, decrease wait times, and reduce healthcare delivery costs. The company has raised $300M+ from top-tier investors including Bain Capital, Insight Partners, and Goldman Sachs, and has been named among the top 100 AI companies globally.
The Revenue Engineering team builds internal applications, API integrations, and automation that power the commercial engine. This Associate GTM Engineer role is a software engineering position embedded within the revenue team, focused on bringing proper software engineering practices to systems currently maintained by operations specialists.
You will own and build the technical layer supporting revenue operations, writing code, deploying services, and integrating systems across the commercial stack. The team has deep domain expertise in revenue operations and will teach you the business; what they need are strong software fundamentals, clean code practices, and curiosity about how businesses operate.
Key responsibilities include:
- Converting manual processes across analytics, legal, and commissions into automated, reliable, fully documented data pipelines
- Integrating disconnected CRM, billing, and finance systems using custom APIs, data pipelines, and reconciliation logic
- Deploying containerized AWS services and leveraging AI coding tools to support deal desk, quoting, enablement, and sales operations
- Eliminating recurring manual tasks such as lead enrichment, data hygiene, and quota adjustments
- Introducing proper software practices including version control discipline, testing, error handling, and system monitoring
- Maintaining and optimizing system handoffs between Sales, Implementation, Contracting, Legal, and Finance
- Rapidly troubleshooting data issues, pipeline failures, and system edge cases
- Creating clear documentation and cross-training team members to eliminate single points of failure
- Working directly with non-technical stakeholders to gather requirements, demo builds, manage expectations, and translate business needs into technical solutions
You will have direct influence over technical standards from day one and will bridge the gap between business and engineering perspectives.
REQUIREMENTS:
- Bachelor's degree in Computer Science or related field, or equivalent practical experience
- 0–2 years of professional software experience
- Working fluency in Python and/or JavaScript/TypeScript
- Comfort with SQL and relational data models
- Experience consuming and integrating REST APIs, including authentication, pagination, and error handling
- Familiarity with version control and exposure to code deployment in real environments (containers, CI, logging, monitoring)
- Daily use of AI-assisted development tools (Cursor, Claude Code, GitHub Copilot) as part of normal workflow
- Demonstrable interest in the commercial side of business (sales, revenue, pricing, growth) through coursework, side projects, startup experience, or internships
- Genuine interest in healthcare operations and improving health system efficiency
- Excellent written and verbal communication skills, including documentation, requirements gathering, status updates, and clear explanations for non-technical audiences
- Ability to run working sessions with stakeholders, demo builds, surface requirements, and respectfully push back when needed
BONUS EXPERIENCE:
- Salesforce developer/admin experience (Apex, SOQL, Flow) or HubSpot with native API integrations and certifications
- Practical understanding of Revenue Operations and revenue mechanics (ACV, ARR, quotas, commissions, billing, quoting systems)
- Workflow automation using iPaaS platforms (n8n, Zapier, Workato, Make, Notion API)
- Data engineering and analytics background (Snowflake, BigQuery, dbt, ETL pipelines, BI platforms)
- Exposure to modern GTM tooling (Clay, ZoomInfo, Apollo, Outreach, Gong, Marketo)
- Hands-on DevOps experience with Docker, AWS, CI/CD pipelines, or Infrastructure-as-Code
- Generative AI development experience beyond coding assistants (LLM APIs, RAG, embeddings, vector search, AI frameworks)
