• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts • Translate weak signals and technical possibilities into clear product hypotheses • Explore ideas before there is a clear buyer, category, or demand signal • Maintain an exploration backlog with risks, assumptions, and learning goals • Frame experiments around the single riskiest assumption • Define explicit kill criteria before building anything • Execute scrappy prototypes, MVPs, and pilots with minimal scope • Synthesize results into opinionated recommendations: Scale / Iterate / Kill • Clearly communicate what was tested, what was learned, and what remains unknown • Maintain a visible Lab portfolio: what's being explored, why, and what the signals say

• Own and evolve squad product roadmap from discovery through delivery • Be in the driver's seat alongside design and engineering — not as a spec-writer, but as a builder who leads product direction and uses AI to move faster, go deeper, and contribute to a fast based, customer-obsessed team • Stay deeply informed on the AI developer tools landscape (Cursor, Copilot, Claude Code, and what’s next) • Define success metrics for AI features upfront and drive iteration based on data • Actively engage customers to gather feedback and translate it into product decisions • Solve customer problems directly — not just through product specs, but sometimes by hand; we believe in doing things that don't scale until you've learned enough to build them at scale • Use AI tools actively in your own daily workflow — to research, prototype, write, and build

• Collaborate with ecosystem partners to identify opportunities for accelerating usage and adoption of Circle’s digital assets • Translate ecosystem insights into actionable product initiatives; define and own the product vision and roadmap from 0 to 1 • Conduct market research to uncover emerging use cases, trends, and high‑potential digital assets • Stay at the forefront of developments in on‑chain asset issuance and new digital asset use cases • Lead cross‑functional teams (engineering, legal, data science, marketing, business) to ship products to market

• Own the product roadmap for enterprise platform capabilities, including resilience, fault tolerance, high availability, data consistency, and disaster recovery features • Work closely with core platform engineering teams building distributed data store and streaming analytics capabilities in a Java-based, open-source environment • Partner with engineering to define, scope, and deliver features that meet the demanding reliability and performance requirements of Fortune 500 customers • Collaborate with Engineering and QA teams to ensure performance, scalability, and resiliency targets are met for customer workloads at scale • Engage with the open-source community and ecosystem to inform product direction, gather feedback, and drive adoption of Hazelcast’s open-source offerings • Become a deep product expert in Hazelcast’s real-time data platform, including in-memory data grid, streaming, and caching capabilities • Collaborate cross-functionally with Documentation, Solution Architects, Sales Engineering, and Product Marketing to translate technical capabilities into customer value • Work effectively across time zones with Europe-based engineering teams, including regular overlap with CET/GMT working hours • Champion the adoption of AI-powered tooling and agentic workflows across product management and engineering processes—from AI-assisted product discovery and specification writing to leveraging coding agents and LLM-based tools to accelerate development velocity • Build strong trust with distributed teams and effectively negotiate backlog prioritization and release planning