• Partner closely with leaders across Product, Science, Engineering, Marketing, and Operations to transform data into actionable insights that drive business growth and scientific discovery. • Manage and mentor a growing analytics team, build scalable frameworks, and help foster a culture of data-driven decision-making across the organization. • Design, build, and maintain our data models and pipelines, enabling self-service analytics and empowering teams across Seed to make data-informed decisions with confidence. • Contribute to the evolution of Seed’s analytics roadmap in alignment with company priorities. • Establish frameworks, standards, and best practices for reporting, modeling, and experimentation. • Partner with cross-functional leaders to design analyses, reports, and dashboards that inform strategy and execution. • Build scalable pipelines and data models that support self-service analytics across teams. • Define and maintain processes for data quality, accuracy, and consistency across systems. • Provide insights that optimize acquisition, retention, and customer experience.

• Join a team of Analytics Engineers that will develop data pipelines and use SQL- and Python-based ETL frameworks to acquire, process, and deliver data to consumers around SeatGeek • Communicate and collaborate with different stakeholders, like Data Scientists, Analysts, Data Platform Engineers, and Software Engineers to understand data use cases and product requirements for R&D teams • Architect and thoroughly QA performant data models that enable analysis in a data warehouse setting • Assist in triaging and debugging on-call requests in a rotational on-call schedule with the Analytics Engineering team • Enable AI-assisted stakeholder data analysis by designing LLM-friendly DWH schemas, creating domain guides, and enforcing canonical naming conventions across all tables to be consumed by the AI agent • Partner with Analytics leadership to develop a strategy around BI tooling to ensure stakeholders have easy access to usable data

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are looking for an Analytics Engineer to join SeatGeek’s Data team. You will join a high-performing team that partners closely with business stakeholders for their data management and insights generation needs. Analytics Engineers play a critical role in discovering, extracting, storing, cataloging, modeling, and processing our data. As a member of this team you will own our analytics data layer. - Extract data from production databases as well as third-party sources - Architect efficient data models - Orchestrate jobs in Airflow to produce clean, well-documented, and analysis-friendly datasets - Own relationships with both analysts and production engineers to discover the value of our data - Drive us toward our mission of making SeatGeek the top destination for live event goers Qualifications - 3+ years of relevant experience in Analytics, Data, or Software Engineering - Experience with DBT, databricks, or similar SQL-transformation tool is a must - A bachelor’s degree or higher in economics, psychology, computer science, statistics, mathematics or another quantitative discipline - Expert-level knowledge of SQL - Proficiency in programming with Python - Experience developing new metrics and performing analysis with large datasets - Experience cleaning data sets, developing new models and interfacing with stakeholders to understand business requirements - Expert verbal and written communication skills Requirements - Communicate and collaborate with different stakeholders, like Data Scientists, Analysts, Data Platform Engineers, and Software Engineers to understand data use cases and product requirements for R&D teams - Architect and thoroughly QA performant data models that enable analysis in a data warehouse setting - Assist in triaging and debugging on-call requests in a rotational on-call schedule with the Analytics Engineering team - Enable AI-assisted stakeholder data analysis by designing LLM-friendly DWH schemas, creating domain guides, and enforcing canonical naming conventions across all tables to be consumed by the AI agent - Partner with Analytics leadership to develop a strategy around BI tooling to ensure stakeholders have easy access to usable data Benefits - Equity stake - Flexible work environment, allowing you to work as many days a week in the office as you’d like or 100% remotely - A WFH stipend to support your home office setup - Unlimited PTO - Up to 16 weeks of fully-paid family leave - 401(k) matching - Student loan matching program - Health, vision, dental, and life insurance - Up to $25k towards family building, reproductive health services and Gender-affirming care - $500 per year for wellness expenses - Subscriptions to Headspace (meditation), Headspace Care (therapy), and One Medical - $120 per month to spend on tickets to live events - Annual subscription to Spotify, Apple Music, or Amazon music

• Own the end-to-end implementation of data solutions, from problem definition to production deployment • Design scalable data models and pipelines that support business-critical use cases • Improve performance, reliability, and cost-efficiency of our Databricks environment • Lead technical decisions around data architecture and implementation patterns • Ensure production-grade quality through testing, code review, observability, and monitoring • Partner with functional teams to clarify requirements and deliver 1–2 high-impact data solutions • Review the current data platform and produce a short, actionable improvement plan • Build and productionize core datasets in Databricks across Bronze/Silver/Gold