• Take ownership of service analytics and provide ongoing analytical support for product, marketing, and web teams • Work with existing data foundations, dashboards, and metrics, improving and scaling them where needed • Answer ad-hoc and recurring business questions related to product performance, marketing efficiency, and growth • Maintain and develop performance, ROI, and attribution analytics in collaboration with product and growth teams • Ensure data quality, metric consistency, and clear documentation for key dashboards and reports • Communicate insights and recommendations clearly to both technical and non-technical stakeholders

• Facilitate data-informed decision-making across the product and engineering teams • Partner with product & engineering teams understand important business problems, then own the full lifecycle of data-driven solutions: analysis, metric definitions, transformation pipelines in dbt, experimentation, measurement, data story-telling and, most importantly, influencing decisions • Collaborate with Product, Research, Engineering, Customer Success and others to provide data products to allow them to better measure the impact of their work and find new opportunities • Work with data engineers on our team to help build our new Lake House in Databricks in your area of expertise and document vital context about our data that can be used by both stakeholders and AI tools • Provide leadership in how we use product analytics tools like Amplitude to make decisions • Be a key voice in making data-driven decisions in our software development lifecycle • Increase your impact by continuously learning and evolving your workflows as AI technology matures. Form opinions that help Jellyfish support its customers in their AI journeys. • Enable your stakeholders to have a bigger impact through the use of AI in their own analytics workflows. As necessary, build the data tools they need, be their expert-in-the-loop, or influence them to adopt new workflows.

• Query, extract, and transform data from multiple sources to support business needs • Build and maintain automated reports, dashboards, and data pipelines • Partner with cross-functional teams to understand their goals • Identify patterns, trends, anomalies, and opportunities in data sets • Develop and maintain Python scripts for data automation and reporting • Contribute to improving data infrastructure, documentation, and analytical best practices • Explore opportunities to incorporate AI-powered tools

• Take ownership of service analytics and provide ongoing analytical support for product, marketing, and web teams • Work with existing data foundations, dashboards, and metrics, improving and scaling them where needed • Answer ad-hoc and recurring business questions related to product performance, marketing efficiency, and growth • Maintain and develop performance, ROI, and attribution analytics in collaboration with product and growth teams • Ensure data quality, metric consistency, and clear documentation for key dashboards and reports • Communicate insights and recommendations clearly to both technical and non-technical stakeholders