Sr. Product Manager - Skylar Analytics
New
J
JobgetherSaaS Analytics
United StatesFull-TimeSenior
Salary$135,000–$155,000 annually
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Job Details
- Experience
- 5+ years
- Required Skills
- SQLBusiness IntelligenceMachine LearningProduct ManagementJiraData modelingSaaSData analytics
Requirements
- 5+ years of Product Management experience focused on reporting, analytics, business intelligence, or a closely related data product, with a demonstrated history of delivering accessible dashboards and reporting solutions.
- Strong understanding of data modeling concepts, including dimensional modeling, OLAP cubes, schema design, analytical architectures, and query-performance tradeoffs.
- Hands-on SQL proficiency, with the ability to write queries, inspect data, and validate assumptions independently.
- Familiarity with modern data-stack technologies such as ClickHouse, dbt, data warehouses, and analytical data pipelines.
- Experience with BI platforms such as Superset, Power BI, Tableau, or comparable enterprise reporting tools.
- Working knowledge of data science and machine-learning concepts, particularly anomaly detection and ML pipelines, with the ability to collaborate effectively with technical specialists.
- Experience leading go-to-market initiatives for technical products and translating complex platform capabilities into compelling customer benefits.
- Experience with multi-tenant SaaS products and an understanding of the operational and architectural considerations they involve.
- Experience using Jira Software for product planning and collaboration.
- Strong communication, prioritization, stakeholder-management, and strategic thinking skills, with the ability to work effectively across product, engineering, data, sales, and marketing teams.
Responsibilities
- Own the product vision, strategy, and roadmap for the analytics and reporting platform, covering data modeling, customer-facing reporting, query capabilities, and data access.
- Define and deliver connectors, data structures, and reporting templates for platforms such as Power BI and Superset, helping customers access operational data without requiring custom development.
- Collaborate closely with data engineering and data science teams to establish and continuously improve an extensible operational data model aligned with customer needs.
- Partner with the broader product organization to define and maintain data pipeline contracts, including data scope, fidelity, and evolution as connected products develop.
- Drive the customer experience for anomaly detection and predictive alerting by determining which machine-learning outputs are surfaced and how customers can act on them.
- Identify opportunities to strengthen shared data infrastructure and enable analytics data to support emerging agentic AI capabilities.
- Lead go-to-market strategy, including positioning, packaging, messaging, and the customer narrative that establishes the analytics offering as a compelling standalone product.
- Partner with sales, marketing, engineering, and other stakeholders to translate complex technical capabilities into clear customer value and measurable business outcomes.
- Use customer feedback, product data, and market insights to prioritize enhancements and continuously improve adoption, usability, and time-to-value.
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