Note: The job is a remote job and is open to candidates in USA. SingleStore is a cloud-native database company that empowers digital leaders to deliver exceptional, real-time data experiences. The Principal Product Manager Lead will own the strategy, roadmap, and execution for a portfolio that includes AI text-to-SQL and data analysis, focusing on enhancing product capabilities and customer engagement through innovative analytics solutions.
Responsibilities
- Owns Aura Analyst as the primary end-user tool for AI-guided text-to-SQL and conversational query result analysis, including technical feature set definition, customer positioning, and roadmap
- Co-owns the product strategy for AI and ML capabilities exposed as AI functions, ML functions, Python UDFs, Cloud Functions, Container Services, MCP server, and AI documentation question answering (SQrL)
- Ensures these surfaces are observable, testable, and debuggable, with clear workflows for data engineers and application developers
- Aligns AI/ML function capabilities with Aura Analyst so that AI workloads are first-class citizens in observability and performance views
- Owns product strategy for query optimization, tuning, and AI-based database tuning, in close collaboration with the core engine team
- Defines how query plans, regressions, and recommendations are surfaced in the UI, APIs, and internal tools
- Partners with engine leadership on prioritization of query engine investments that materially improve customer performance, reliability, and cost
- Owns database observability, cloud containers observability, and alerting experiences used by both customers and internal SRE/support teams
- Partners with data and analytics teams on the internal product analytics and reporting stack powering dashboarding on product adoption and COGS
- Develops and maintains a multi-release roadmap for Aura Analyst, SingleStore productivity AI Agents, skills and MCP servers, AI/ML functions, query optimization, and observability & alerting, aligned with company strategy and product positioning
- Defines clear problem statements, success criteria, and scope for major initiatives; maintains a prioritized backlog across teams
- Acts as the product lead across multiple engineering and design teams (engine, cloud, AI platform, SRE, support, DevX)
- Directly manages an analytics engineer responsible for building and maintaining reporting on cloud platform operations, product usage, and key business metrics; provides prioritization, feedback, and career coaching
- Provides product direction and mentorship to PMs working in adjacent areas (AI, DevX, analytics)
- Ensures initiatives in this portfolio are well understood and sequenced appropriately in planning cycles
- Regularly meets with key customers and design partners to validate problems, designs, and proposed solutions in this portfolio
- Serves as a primary point of contact for the field (Sales, Solutions, CS) on performance, observability, and AI-diagnostics-related topics
- Partners with Support and SRE to translate recurring operational pain (e.g., incidents, hot spots, noisy alerts) into product requirements
- Writes product requirements documents and detailed requirements for new features and enhancements; reviews technical design documents and UX designs to ensure alignment with product goals
- Drives end-to-end feature delivery: preview programs, documentation coordination, field enablement, and launch readiness
- Ensures we have clear “definition of done” and acceptance criteria for features in this portfolio
- Defines and tracks key metrics and leading indicators, including (examples, not exhaustive): Time-to-detect (TTD) and time-to-resolve (TTR) for incidents; Query performance and resource efficiency metrics (CPU, memory, GPU); Adoption, engagement, and retention for Aura Analyst and AI/ML functions; Volume and severity of support tickets related to performance and observability
- Works with analytics and data engineering teams to ensure telemetry, internal views, and dashboards exist to measure these outcomes
- Uses data and qualitative feedback to iterate on UX, feature behavior, and defaults
- Ensures experiences across Aura Analyst, AI functions, query insights, and observability feel cohesive, not separate tools, especially for workflows like: AI-guided querying and analysis; Debugging slow queries or incidents; Understanding resource consumption and cost; Operating AI and ML features in production
Skills
- Develops and maintains a multi-release roadmap for Aura Analyst, SingleStore productivity AI Agents, skills and MCP servers, AI/ML functions, query optimization, and observability & alerting, aligned with company strategy and product positioning
- Defines clear problem statements, success criteria, and scope for major initiatives; maintains a prioritized backlog across teams
- Acts as the product lead across multiple engineering and design teams (engine, cloud, AI platform, SRE, support, DevX)
- Directly manages an analytics engineer responsible for building and maintaining reporting on cloud platform operations, product usage, and key business metrics; provides prioritization, feedback, and career coaching
- Provides product direction and mentorship to PMs working in adjacent areas (AI, DevX, analytics)
- Ensures initiatives in this portfolio are well understood and sequenced appropriately in planning cycles
- Regularly meets with key customers and design partners to validate problems, designs, and proposed solutions in this portfolio
- Serves as a primary point of contact for the field (Sales, Solutions, CS) on performance, observability, and AI-diagnostics-related topics
- Partners with Support and SRE to translate recurring operational pain (e.g., incidents, hot spots, noisy alerts) into product requirements
- Writes product requirements documents and detailed requirements for new features and enhancements; reviews technical design documents and UX designs to ensure alignment with product goals
- Drives end-to-end feature delivery: preview programs, documentation coordination, field enablement, and launch readiness
- Ensures we have clear 'definition of done' and acceptance criteria for features in this portfolio
- Defines and tracks key metrics and leading indicators, including (examples, not exhaustive): Time-to-detect (TTD) and time-to-resolve (TTR) for incidents, Query performance and resource efficiency metrics (CPU, memory, GPU), Adoption, engagement, and retention for Aura Analyst and AI/ML functions, Volume and severity of support tickets related to performance and observability
- Works with analytics and data engineering teams to ensure telemetry, internal views, and dashboards exist to measure these outcomes
- Uses data and qualitative feedback to iterate on UX, feature behavior, and defaults
- Ensures experiences across Aura Analyst, AI functions, query insights, and observability feel cohesive, not separate tools, especially for workflows like: AI-guided querying and analysis, Debugging slow queries or incidents, Understanding resource consumption and cost, Operating AI and ML features in production
- Deep understanding of distributed databases, query processing, and performance optimization, with comfort reading query plans, indexes, and workload patterns
- Working knowledge of, and experience delivering AI Agents, skills, and/or MCP servers
- Strong familiarity with observability and alerting systems and SRE/operations workflows
- Demonstrated ability to work effectively with senior engineering leaders and architects on complex technical decisions and trade-offs
- Strong execution skills: able to drive complex, multi-quarter initiatives across multiple teams while maintaining clarity on goals, ownership, and status
- Excellent communication skills, with the ability to present complex technical topics to executives, field teams, and customers in a clear, outcome-oriented way
- Willingness and ability to mentor other PMs and act as a go-to leader for performance, diagnostics, and AI/observability-related product questions
Company Overview
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