Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLArtificial IntelligenceMachine LearningSnowflakeProduct AnalyticsData modelingDatabricks
Requirements
- 5+ years of experience in product data, product analytics, or analytics engineering within SaaS or technology environments.
- Proven experience defining and implementing product instrumentation and telemetry systems at scale.
- Strong background in building or owning event pipelines and first-party product data systems.
- Experience designing governed, reusable data assets such as metrics layers, datasets, or semantic models.
- Hands-on expertise with modern data platforms such as Databricks or Snowflake.
- Strong SQL and Python skills for data analysis, modeling, and pipeline development.
- Experience working closely with Product and Engineering teams on measurement frameworks and data-driven decision-making.
- Demonstrated leadership experience managing or building small, high-impact teams.
- Strong communication and stakeholder management skills, with the ability to influence both technical and non-technical audiences.
- Experience with product analytics tools such as Amplitude, Pendo, or similar platforms is a plus.
- Familiarity with AI or machine learning applications in product or behavioral data is desirable.
- Experience in multi-product or M&A environments is an advantage.
- Background in EdTech, assistive technology, or mission-driven organizations is highly valued.
Responsibilities
- Define and lead the global product data and telemetry strategy, aligning data collection with business, product, and learner impact objectives.
- Own the telemetry and event architecture, including event pipeline design, instrumentation standards, and integration into modern data platforms such as Databricks.
- Build and govern scalable product data models, ensuring consistency across a multi-product ecosystem.
- Design and deliver trusted data products such as datasets, dashboards, and metrics frameworks for internal and external use.
- Enable customer-facing analytics and usage transparency as part of the product experience.
- Partner with Data Engineering and Analytics teams on pipelines, data contracts, and data consumption layers.
- Collaborate with Product leaders to define success metrics, measurement frameworks, and outcome tracking.
- Translate product usage data into actionable insights that drive product improvement and business outcomes.
- Build and lead a high-performing product data team while enabling self-service analytics across the organization.
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