Data Analyst
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Requirements
• 2–4 years of experience in a Data Analyst, Analytics Engineer, or similar role. • Strong SQL skills and experience working in a modern cloud data warehouse. • Experience performing exploratory analysis and communicating actionable insights. • Experience working cross-functionally and influencing business stakeholders. • Demonstrated ability to drive measurable impact through data. • Experience building analytical data models — not only dashboards and ad hoc analysis. • Strong written and verbal communication skills. • A proactive mindset and willingness to take on projects outside your comfort zone. • Familiarity with tools such as: • Fivetran, Airbyte, Stitch (or similar ingestion tools) • dbt, SQLMesh, or dataform • A modern BI tool (Tableau, Sigma, Looker, etc) • We currently use Airbyte, BigQuery, SQLMesh, Looker Studio, etc • Familiarity with version-controlled analytics workflows. • Familiarity with dimensional modeling concepts (fact tables, dimension tables, star schemas). • Experience building predictive or inference models. • Experience building and maintaining data ingestion pipelines. • Experience implementing data quality testing and documentation practices. • Experience working with GTM teams to define and report on GTM metrics. • Exposure to event-based or product analytics data. • Experience using AI effectively in your own workflow. • What This Role Is Not • This is not a dashboard-only reporting role. • This is not a pure data engineering position building production infrastructure. • This is not a purely reactive analytics role — we value proactive thinking and ownership. • This is not a role where you work in isolation. You’ll collaborate closely with Product and Engineering. • BS in Computer Science, Statistics, Data Science, Mathematics, Engineering, or a related quantitative field (or equivalent practical experience). • 2–4 years of relevant professional experience in analytics. • Proficiency in SQL required; experience with modern analytics tooling strongly preferred. • Demonstrated experience working cross-functionally with Product, Engineering, Customer Success, or business stakeholders.
Responsibilities
• Partner cross-functionally to define KPIs, answer strategic questions, and support decision-making. • Build reporting and dashboards that drive action — not just monitoring. • Present insights clearly to both technical and non-technical stakeholders. • Perform data analysis to uncover trends, risks, and growth opportunities. • Define and maintain core business metrics to ensure alignment across teams. • Support and improve dimensional data models (fact and dimension tables) in the warehouse. • Build and maintain reliable data transformations to ensure accuracy and consistency. • Help improve data quality, documentation, and reporting standards. • Contribute to forecasting, experimentation analysis, and predictive or inference modeling initiatives.
Benefits
• Team: Engineering • Level: Intermediate (2–4 years experience) • As Jump continues to grow, the quality of our data analytics foundation directly impacts product decisions, operational efficiency, and customer outcomes. This role ensures our metrics are trustworthy, our models are scalable, and our decisions are grounded in structured, defensible analysis. • Strong analytics compounds. The work you do here will shape how we define success, measure performance, and prioritize what we build next.