The outcomes you'll deliver
- Trusted product data foundation: Build reusable data models, transformation layers, and pipelines that become the reliable source of truth for the Video product team while adhering to shared Data Platform standards.
- Scalable experimentation framework: Establish the data and analysis infrastructure that enables teams to run, measure, and iterate on A/B tests consistently and efficiently.
- Production-ready data products: Deliver APIs, dashboards, and data products that power customer-facing experiences and provide stakeholders with trusted metrics for decision making.
- Reliable product instrumentation: Ensure event tracking, data contracts, testing, and monitoring are in place so product behavior is captured accurately and remains trustworthy over time.
- Predictive insights that drive growth: Develop predictive and causal models that improve forecasting, retention, pricing, and product strategy.
In this role, you can expect to
- Build experiences our customers love by embedding in the Video product org alongside PMs, designers, and engineers.
- Strengthen and grow the data foundation using software discipline such as version control, review, and testing.
- Build experimentation into a system that scales to support A/B testing infrastructure.
- Get instrumentation right at the source through event tracking and data contracts.
- Power product with live data APIs, owning endpoints from specification through production.
- Surface insights and drive decisions by building trusted dashboards and metrics.
- Develop predictive and causal models, including LTV forecasting and propensity modeling for retention and revenue.
To thrive in this role, you have
- Strong SQL skills and comfort working with large analytical datasets (complex joins, window functions, and performance tuning).
- Strong data modeling instincts and a track record of clean, documented, reusable transformation layers (SQLMesh, dbt, or equivalent).
- Production-grade Python for modeling, orchestration, and statistical analysis.
- Experience building data products others depend on: APIs, pipelines, and dashboards.
- Stakeholder fluency to translate business questions into metrics and influence decisions.
- Familiarity with experimentation and A/B testing methodologies.
- Familiarity with AI-augmented development tools as part of a modern workflow.