Principal Product Manager Lead

About this role

Position Overview

This role is a principal-level, hands-on product leadership position in the Product organization. It owns the end-to-end strategy, roadmap, and execution for a cross-cutting portfolio spanning AI text-to-SQL and data analysis (Aura Analyst), AI and ML functions, query optimization and tuning, and database / cloud platform observability & alerting.

A unifying theme of this role is analytics, including unlocking latent demand in our customers using AI-driven query and analysis, making the core database engine perform analytical workloads better via query optimization, and enabling us and customers to analyze system telemetry to make workloads shine. If you love analytics, you're an experienced product leader, and want to build an innovative, AI-enriched product and business, not groom the backlog, this job is for you.

A major company focus is to surround SingleStore with AI assistance to make everyone using or building on SingleStore more productive. This includes allowing data to be queried and analyzed far more easily by a much broader range of people, and making SingleStore increasingly self-observing, self-diagnosing, and self-optimizing. This helps customers and internal teams use and analyze their data, understand workload behavior, debug issues quickly, and continuously improve performance and efficiency. This role leads product management for a range of AI analysts, skills, and MCP servers to provide these capabilities.

Role and Responsibilities

Primary product areas:

• Aura Analyst

• 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.

• AI & ML Functions Platform

• 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.

• Query Optimization & Tuning

• 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.

• Observability, Alerting & Internal Data Warehouse

• 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.

Required Skills and Experience

Strategy & roadmap

• 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.

Cross-functional leadership & people management

• 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.

Customer, field, and internal stakeholder engagement

• 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.

Execution and delivery

• 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.

Metrics and continuous improvement

• 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.

Experience quality and cohesion

• 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.

Expect

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