Attio
Solutions Engineer [Pre and Post-Sales] - Mid-Market
About this role
Attio is seeking a Solutions Engineer to serve as the technical point of contact for mid-market customers throughout their lifecycle with the AI-powered CRM platform. You'll architect custom solutions, lead technical demonstrations, and guide customers through complex implementations while partnering with sales and advocating for customer needs internally.
What you'll do
- Master Attio's product capabilities and deploy solutions across industries and departments
- Architect custom solutions using data models, workflows, APIs, reporting, and third-party integrations
- Conduct product demos, training sessions, and technical consulting with customers
- Support Account Executives during pre-sales and close major deals
- Manage post-sale onboarding and serve as primary technical contact for top accounts
- Provide customer insights to internal product, engineering, and marketing teams
What they're looking for
- 2+ years B2B SaaS experience managing customer technical relationships
- SaaS deployment architecture across varied industries and use cases
- API integration and no-code development platform expertise
- GTM tech ecosystem knowledge
- RevOps and sales technology best practices
- Technical communication and training ability
- CRM and data modeling fundamentals
- Adaptability in hybrid sales and customer success roles
Benefits
- Competitive salary of $160,000–$200,000 OTE with 70/30 commission split
- Equity stake in early-stage, well-funded tech company
- Hybrid work flexibility with occasional in-person customer visits
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Attio
Attio builds an AI-powered CRM platform designed to help companies manage customer relationships and go-to-market operations. The company is hiring Solutions Engineers and Forward Deployed Engineers to support customers across SMB, mid-market, and enterprise segments through implementation, custom integrations, and technical advocacy.
- Website
- attio.com
Likely interview questions
- Walk us through a complex customer deployment you architected—what were the technical components, and how did you handle competing requirements across their data model and integrations?
- How do you balance being a trusted technical advisor to customers while also representing their feedback back to our product and engineering teams?