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 analyses
• Write performant SQL against a cloud data warehouse (Snowflake, BigQuery, or Redshift) to answer product, growth, and operational questions
• Build and maintain dashboards in Looker, Tableau, or Mode for leadership and cross-functional partners, with documented metric definitions
• Own end-to-end analyses - cohort retention, funnel conversion, A/B test readouts, unit economics - from framing through presentation
• Partner with data engineers on data quality: set up monitoring, validate dbt model outputs, and triage pipeline anomalies
• Translate ambiguous stakeholder requests into clear analytical questions, deliverables, and timelines
• Document 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 engineering
• Expert-level SQL and strong proficiency in Python (pandas, numpy) for analysis and data manipulation
• Hands-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 Mode
• Solid statistics fundamentals: hypothesis testing, regression, experiment analysis, and common analytical pitfalls
• Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
• Bonus: experience with data visualization libraries (Plotly, Streamlit), reverse ETL tools, event tracking design (Segment, Amplitude), or owning an A/B testing program