Data Analyst

About The Role The role sits at the intersection of data engineering and business analytics - building reliable, well-modeled data assets and turning them into analyses that drive product, growth, and operations decisions. The Data Analyst will work closely with analytics engineers, data engineers, and cross-functional stakeholders to define metrics, instrument them correctly, and ship dashboards and analyses that leadership actually uses. The role matters because decisions at the company are made on data, and this is one of the people accountable for making that data trustworthy. Key Responsibilities Design, build, and maintain metrics layers and semantic models in dbt, ensuring consistent definitions across dashboards and ad-hoc analysesWrite performant SQL against a cloud data warehouse (Snowflake, BigQuery, or Redshift) to answer product, growth, and operational questionsBuild and maintain dashboards in Looker, Tableau, or Mode for leadership and cross-functional partners, with documented metric definitionsOwn end-to-end analyses - cohort retention, funnel conversion, A/B test readouts, unit economics - from framing through presentationPartner with data engineers on data quality: set up monitoring, validate dbt model outputs, and triage pipeline anomaliesTranslate ambiguous stakeholder requests into clear analytical questions, deliverables, and timelinesDocument analyses, metric definitions, and methodologies so findings are reproducible and auditable What We Are Looking For 3-6 years of experience in data analytics, business intelligence, or analytics engineeringExpert-level SQL and strong proficiency in Python (pandas, numpy) for analysis and data manipulationHands-on experience with dbt and a modern data warehouse (Snowflake, BigQuery, or Redshift)Track record of shipping dashboards and self-serve analytics in Looker, Tableau, or ModeSolid statistics fundamentals: hypothesis testing, regression, experiment analysis, and common analytical pitfallsBachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experienceBonus: experience with data visualization libraries (Plotly, Streamlit), reverse ETL tools, event tracking design (Segment, Amplitude), or owning an A/B testing program