Growth Engineer

Growth Engineer

Builds, automates, and experiments with code-driven and AI-driven systems that turn every prospect touch into measurable pipeline and revenue.

Why This Role Exists

In a sales-first SaaS company, engineering should behave like the highest-leverage SDR on the floor. Your code—and the AI agents you deploy—compress the time from first touch to SQL, slash manual work, and surface insights that let sellers focus on closing, not clicking.

1. SDR Workflow Automation

• Build low-code automations that:
  

  • Enrich inbound leads via third-party data + an LLM entity-resolution agent.
    

  • Auto-assign leads to the right SDR by routing logic trained on historical win-rates.
    

  • Trigger AI SDR assistants (voice, chat, email) that handle first-touch outreach and book meetings when prospects reply after hours.

2. Conversation & Call Intelligence

• Integrate speech-to-text + summarization agents into dialer/Zoom calls so reps get real-time objection handling prompts and post-call summaries pushed into the CRM.
  

3. AI-Driven Lead Scoring & Prioritization

• Deploy and maintain rule-/ML-based scoring models with feature pipelines that combine product-usage events, firmographics, and AI sentiment analysis from rep calls.
  

• Surface “hot lead” alerts directly inside the SDR’s workflow (HubSpot/Salesforce, Outreach, Hatch).
  

4. Data Instrumentation & Pipeline Health

• Own event tracking (Segment / Snowplow) and orchestrate ELT jobs into the warehouse.
  

• Use anomaly-detection agents to flag broken events or sudden metric swings within minutes, not days.
  

5. Growth Infrastructure Ownership

• Maintain lightweight microservices (e.g., FastAPI, Next.js) that support referral programs, pricing tests, and personalized recommendations.
  

• Design services to expose clean API endpoints so AI agents can consume and act on real-time data (e.g., “draft follow-up email” or “update opportunity stage”).
  

6. Cross-Functional Alignment

• Pair with Sales Ops, Marketing, and RevOps to turn funnel diagnostics into prioritized engineering sprints and AI playbooks.
  

• Present weekly experiment readouts—highlighting causal links between shipped code, AI interventions, and pipeline impact.
  

7. Metrics Ownership

• Ship dashboards that track lead velocity, SQL conversion, pipeline ARR, and AI agent performance (response quality, handle-rate).
  

• Iterate on models and agents until you see statistically significant lifts in qualified pipeline and reduced SDR time-to-touch.
  

Must-Have Skills & Experience

• 3-5 years building production web services or data pipelines (Python/TypeScript preferred).
  

• Demonstrated success shipping growth or experimentation code in a SaaS environment.
  

• Hands-on experience with LLMs, orchestration frameworks (LangChain, LlamaIndex), and vector stores.
  

• Fluency with SQL and cloud data warehouses (Snowflake, BigQuery, Redshift) plus ELT tools (Fivetran, Airbyte).
  

• Deep knowledge of modern sales stacks (Salesforce/HubSpot, Outreach/Salesloft, dialers, enrichment APIs).
  

• Ability to translate commercial hypotheses into quick prototypes, then into fully monitored, revenue-producing systems.
  

Nice-to-Haves

• Built or integrated voice, chat, or email AI agents that carry prospect conversations end-to-end.
  

• Experience merging PLG usage signals with sales-assist motions.
  

• Exposure to real-time recommendation or reinforcement-learning systems that optimize SDR sequences.
  

How You’ll Be Measured

1. Qualified Pipeline Impact – incremental ARR pipeline attributable to code and AI agents you deploy.
   

2. Velocity – average time from hypothesis to live experiment.
   

3. Experiment Win-Rate – % of tests beating control on a primary metric.
   

4. SDR Efficiency – change in meetings booked per SDR hour and lead-to-touch SLA.
   

5. AI Agent Effectiveness – handle-rate, sentiment scores, and revenue contribution from automated conversations.
   

Bottom line: You’re the engineer who treats “more revenue, faster” as your primary feature—leveraging AI agents wherever they create compounding leverage for the sales team.