Solutions Engineer - Partnerships
HebbiaNew York CityPosted 15 April 2026
Job Description
About Hebbia
The AI platform for investors and bankers that generates alpha and drives upside.
Founded in 2020 by George Sivulka and backed by Peter Thiel and Andreessen Horowitz, Hebbia powers investment decisions for BlackRock , KKR , Carlyle , Centerview , and 40% of the world’s largest asset managers. Our flagship product, Matrix, delivers industry-leading accuracy, speed, and transparency in AI-driven analysis. It is trusted to help manage over $30 trillion in assets globally.
We deliver the intelligence that gives finance professionals a definitive edge. Our AI uncovers signals no human could see, surfaces hidden opportunities, and accelerates decisions with unmatched speed and conviction. We do not just streamline workflows. We transform how capital is deployed, how risk is managed, and how value is created across markets.
Hebbia is not a tool. Hebbia is the competitive advantage that drives performance, alpha, and market leadership.
About the Solutions Engineering Team
The Solutions Engineering team converts buyer interest into enterprise conviction. In complex evaluations where customers are underwriting risk, they structure ambiguous problems, build tailored AI workflows, and validate value inside real data environments. This work directly drives win rates, accelerates deal cycles, and gives executives confidence to adopt — making the team a primary driver of revenue and product credibility, not a sales support function.
As Hebbia moves up-market and into new segments and geographies, every expansion requires deeper technical validation, requiring the Solutions Engineering team to rapidly grow and scale. Their learnings compound into repeatable solution patterns that sharpen positioning and unlock larger deals, turning the function into a core lever of scalable growth.
Role Overview
As a Partnerships Solutions Engineer at Hebbia, you are the technical and strategic lead for activating our data and system partnerships in the field. You operate at the intersection of partnerships, sales, and product — ensuring that Hebbia’s integrations with market data providers and fintech platforms translate into real, differentiated value during pre-sales and customer engagements.
You are responsible for bringing partnerships to life. This means deeply understanding partner datasets and systems, translating them into compelling solution narratives, and embedding them into high-impact evaluations and workflows that accelerate revenue.
You bring clarity to complexity across ecosystems. You understand how financial data flows, how systems interconnect, and how to position Hebbia as the intelligence layer across that stack. You design and deliver integrated solutions that validate value, reduce decision risk, and strengthen both Hebbia’s and our partners’ commercial outcomes.
You operate as a trusted advisor to sophisticated buyers navigating fragmented data environments, tooling sprawl, and high-stakes decisions. Your job is to connect the dots across systems, data, and workflows; and prove why Hebbia is essential infrastructure.
This role is based out of Hebbia’s New York City office in SoHo.
What You’ll Do
Own the technical strategy for partnership-driven deals, from discovery through close
Lead joint pre-sales motions with partners, activating integrations with market data providers, content platforms, and fintech systems
Develop a deep understanding of partner datasets, APIs, and system capabilities, and translate them into differentiated Hebbia solutions
Design and configure integrated AI-powered workflows that combine Hebbia with partner data and systems to create real-world value
Act as the technical face of partnerships in the field, supporting co-selling, joint demos, and strategic accounts
Deliver high-stakes integrated demos and solution narratives to senior stakeholders, showcasing cross-platform value
Identify gaps in integrations or positioning, and drive feedback loops to product and partnership ... (truncated, view full listing at source)
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