Data Analyst

Malbon is hiring a Data Analyst/Engineer to join our Digital team! This role will be based out of our HQ in Santa Monica, CA and report directly to our Director of Growth Marketing. This person will own both sides of the data: building the data models and presenting analysis to business stakeholders (this is not a reporting-only or pipeline-only role). 

Location: Malbon HQ is based in Santa Monica, CA. We are currently operating from a hybrid work schedule of 3 days a week in-office.

What You’ll Do

Data Infrastructure & Modeling

• Build and maintain the pipelines that bring Shopify, ad platform, Klaviyo, GA4, retail/POS, and ERP data into a central warehouse
• Design and own the transformation layer, from staging through mart, with dimensional models, version control, and automated testing
• Own the source-of-truth datasets other teams build on; define and document the business logic behind every core metric
• Work in a Git-based analytics workflow: branch, review, test, and deploy model changes rather than editing production directly
• Monitor pipeline runs and triage failures; tune warehouse cost and query performance through materialization strategy, incremental models, and refresh scheduling

Reporting & Self-Serve BI

• Build and maintain dashboards across ecommerce, marketing, merchandising, retail, and finance
• Establish KPI definitions and reporting standards so the organization works from one version of each number
• Deliver the recurring reporting cadence: daily trading, weekly performance, and monthly business review

Data Quality, Reconciliation & Documentation

• Own reconciliation across every channel revenue comes through (web, POS, retail, app, exchanges, and international) so totals hold up
• Validate that reported figures match source systems before they reach leadership; investigate and resolve discrepancies
• Maintain documentation, data lineage, and metric definitions so the work is durable and easy for others to build on
• Identify where AI and automation can take on repetitive data work, such as reconciliation checks, documentation, and routine analysis, and put those workflows into production
• Flag where the data cannot support a conclusion rather than producing a number that looks confident

Business Analysis & Insight

• Channel efficiency, incrementality, and the gap between platform-reported and actual contribution
• Customer analysis: cohorts, retention, repeat rate, LTV, and CAC payback by acquisition source