Product Design Excellence

• Design AI-empowered industrial experiences that span data storage, modelling and data governance of industrial data.
• Prototype with real data and real models by testing on real world industrial use cases with your users and product counterparts; validate flows under realistic latency, streaming, reliability, and cost constraints.
• Design for probabilistic systems operating on imperfect data: handle schema ambiguity, partial ingestion failures, conflicting signals, and uncertainty.
• Define patterns for human-in-the-loop supervision across data mapping, anomaly detection, enrichment, and agent-driven automation.
• Express ideas at multiple fidelities: from system diagrams and agent/tool contracts to interactive prototypes, micro-workflows, and production-ready UI.
• Partner with product strategists and business designers to evaluate desirability, feasibility, and viability; shape experience KPIs tied to adoption, data quality, reliability, and operational efficiency.
• Tell compelling stories through narratives, visuals, and prototypes that make complex data systems understandable and shippable.
• Lead design sessions and facilitate cross-functional workshops to align around architecture, workflows, and long-term product vision.

Design Execution & Ownership

• Own outcomes end-to-end from discovery through shipped experience, including research plans, flows, information architecture, interaction patterns, and system-level design decisions.
• Operationalize AI in the UX: define agent and tool contracts, escalation paths, monitoring hooks, logging requirements, and feedback loops to continuously improve system quality and cost efficiency.
• Collaborate tightly with engineering to scope increments, de-risk technically complex integrations early, and land high-craft solutions within platform constraints.
• Establish scalable design patterns for data-heavy workflows and evolve the design system thoughtfully as new integration use cases emerge.
• Drive clarity in ambiguous, cross-functional environments and make well-reasoned design decisions that balance user value, reliability, performance, and cost.
• Continuously validate solutions through user research, usability testing, and experiment design; make evidence-based tradeoffs across usability, automation, and operational risk.

Contribute to best practices for AI-native product design and help shape how Data Integrations evolve over time.

• 5+ years of product design experience, with demonstrated ownership of complex, data-heavy or AI-powered systems.
• Strong understanding of AI-native workflows, including prompt design, tool/agent orchestration, RAG patterns, and model/runtime constraints such as latency and token/cost budgets.
• Experience designing products that operate on structured and unstructured data, including ingestion, transformation, schema mapping, or data pipeline workflows.
• Hands-on prototyping experience using real data sources, APIs, SDKs, lightweight code, or no/low-code tools to validate feasibility and user experience.
• Demonstrated ability to design for probabilistic and imperfect systems, including error states, uncertainty handling, trust patterns, and human-in-the-loop supervision.
• Strong user research skills: ability to plan and run studies, synthesize qualitative and quantitative insights, and translate findings into actionable product direction.
• Excellent communication, facilitation, and stakeholder management skills; able to collaborate effectively with engineering-heavy teams and present complex ideas clearly to executives and customers.
• Proven ability to work in ambiguous, fast-paced environments with a bias for action and structured decision-making.
• A portfolio showcasing end-to-end ownership, systems thinking, and thoughtful design of complex workflows.