Senior Product Manager, Data Platform & Intelligence
New
Based in United StatesFull-TimeSenior
Salary$139,000 - $200,000 plus bonus and equity opportunities
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Job Details
- Experience
- 5+ years
- Required Skills
- Machine LearningProduct ManagementSnowflakeData modelingSaaSBigQueryRedshift
Requirements
- 5+ years of product management experience, with significant experience in data platforms, AI infrastructure, analytics products, or intelligence solutions.
- Proven experience delivering AI-powered or data-driven products in SaaS, enterprise software, or technology environments.
- Strong technical fluency with the ability to collaborate effectively with data engineers, machine learning teams, and software architects.
- Experience working with data lake, data warehouse, or analytics platforms such as Snowflake, BigQuery, Redshift, or similar technologies.
- Understanding of data modeling, pipeline architecture, schema evolution, and large-scale data consumption.
- Experience building intelligence products such as recommendations, scoring systems, insights, or predictive capabilities.
- Familiarity with evaluating AI systems and measuring the effectiveness of data and context pipelines.
- Experience operating in environments where data governance, security, compliance, and scalability are critical.
- Strong ownership mindset with the ability to manage priorities, resolve ambiguity, and drive cross-functional execution.
- Ability to make thoughtful tradeoffs between data quality, latency, cost, and reliability.
- Excellent communication skills with the ability to translate technical concepts into clear product strategies.
- Customer-focused and outcome-oriented approach to product development.
Responsibilities
- Own the end-to-end product strategy and roadmap for data platform capabilities and intelligence products.
- Define and launch intelligence solutions, including signals, scores, insights, and recommendations that create measurable value.
- Shape how data is structured, enriched, and delivered to AI systems to ensure accurate and effective outcomes.
- Partner closely with AI engineering, data science, product, and platform teams to drive execution and manage dependencies.
- Build data applications that support product use cases, analytics, AI agents, and operational decision-making.
- Establish success metrics for data products, including adoption, quality, coverage, performance, and customer impact.
- Make strategic decisions around data freshness, reliability, scalability, cost, and governance.
- Evaluate build-versus-buy opportunities and guide integrations with external data technologies.
- Define best practices for data quality, lifecycle management, and responsible AI enablement.
- Communicate complex technical concepts clearly across engineering, product, leadership, and business teams.
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