Product Management Topics
Product leadership, vision articulation, roadmap development, and feature prioritization. Focuses on product strategy and business alignment.
Feature Analysis and Launch Evaluation
Designing and applying evaluation frameworks to measure feature success and inform launch decisions. Topics include defining success metrics, experimentation design and basic A over B testing concepts, setting evaluation timeframes, identifying confounding factors, cohort and funnel analysis, instrumentation requirements, and how to iterate based on results. Candidates should be able to propose metrics, describe trade offs in evaluation design, and explain how launch evaluation influences product prioritization.
Product and Domain Knowledge
Deep, working knowledge of a specific product you would represent, build, or sell: its core features, who the target customers are, and the concrete use cases those customers solve with it. Ability to explain how the product works under the hood, at both a high level and in technical detail, covering major components, data flows, and integration points. Where the product is a complex or enterprise system, this extends to deployment models (for example cloud versus on premise), scalability and capacity planning, resilience and recovery, and any compliance certifications that are actually relevant to its customers; not every product needs this, so calibrate to the product in question rather than assuming it. Knowledge of how the product exposes its capabilities to other systems (APIs, connectors, plugins, or partner integrations) where such mechanisms exist. Preparedness to discuss product positioning, competitive differentiation, the adoption or operational challenges real customers face, roadmap themes, and the success metrics or business outcomes the product is meant to drive. This topic assesses product knowledge, systems thinking, and the ability to reason about trade offs for an existing offering, calibrated to whatever kind of product the candidate's target role actually involves.
Technical Requirements and Specifications
Covers the end to end practice of translating product vision and business goals into clear, actionable technical requirements and specifications that engineering teams can implement. Includes writing product requirement documents and technical specifications with problem statements, success metrics, user and developer personas, API contracts and interfaces, data and schema considerations, functional requirements, and non functional requirements such as performance targets, latency and throughput expectations, scalability goals, reliability targets and service level objectives, security and privacy constraints, backward compatibility, and rollout and migration strategies. Encompasses requirements gathering techniques such as stakeholder identification, discovery conversations, clarifying questions, scoping, constraint identification for budget and timeline, defining measurable acceptance criteria, traceability to business objectives, and documenting assumptions and open questions. Also covers communicating requirements effectively to engineering and cross functional partners, knowing how to be specific without over constraining implementation, iterating requirements as learning emerges, and involving engineers early so they provide technical input and ownership.
Roadmap and Strategic Impact
Evaluate the candidate understanding of team and product roadmaps, how work priorities are set, and how individual projects contribute to long term goals. Expect discussion of feature prioritization, trade offs between short term and long term investments, estimating potential user and business impact, aligning with stakeholders, and communicating how proposed work fits into the roadmap. Candidates should show product sense, ability to identify high leverage opportunities, and methods for quantifying and tracking the strategic impact of their contributions.
MVP & Iterative Release Strategy
Identifying minimum viable product scope that delivers core value while managing complexity and timelines. Thinking iteratively about phased releases, learning from initial feedback, and evolving based on data. Distinguishing between MVP and fully-baked solutions. Considering what must be built for launch versus what can be added in phases.
Think Big
Envisioning ambitious solutions and opportunities beyond incremental improvements, understanding strategic impact, and proposing changes that scale. For more junior candidates this includes contributing innovative ideas and identifying efficiency gains that improve processes or customer experience.
Product and Business Impact
Assesses understanding of how technical decisions affect product experience and business metrics. Topics include marketplace dynamics, user needs and behavior, conversion and retention considerations, prioritizing work by impact, experiment and metric design, and connecting engineering trade offs to measurable product outcomes. Candidates should demonstrate curiosity about business drivers and the ability to incorporate product and metric thinking into technical planning.
Customer Obsession for Players
Apply the customer obsession mindset to players by centering decisions around player experience and measurable player outcomes. Discuss key signals such as retention, engagement, session length, churn, conversion, net promoter score, community feedback, and qualitative playtest insights. Describe how you would prioritize features that improve player experience, how to instrument telemetry and run split testing to validate hypotheses, and how to balance short term gains with long term player trust and fairness. Include examples of trade offs considered, metrics used, and how player feedback influenced iterations.
Think Big and Be Right Often
Describe long horizon technical and product thinking and examples where strategic bets delivered measurable impact. Candidates should explain how they identify high leverage opportunities, frame trade offs between short term delivery and long term platform health, prototype to reduce risk, and use data and judgment to validate direction. Interviewers look for pattern recognition, clarity about what success looks like, and concrete outcomes that show effective long term decision making.