Data Analyst / Engineer

Malbon•Santa Monica, CA

21h•Hybrid

About The Position

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.

Requirements

4+ years in a data analyst, analytics engineer, or business intelligence role, with meaningful time at a DTC or ecommerce brand
Bachelor's degree in a quantitative field or equivalent practical experience
Direct experience with ecommerce source data, including Shopify order and customer schemas, ad platform reporting, and an email/SMS platform
Advanced SQL, including CTEs, window functions, and query optimization against large tables
Hands-on experience with a transformation framework (dbt or equivalent) and a cloud data warehouse (Snowflake, BigQuery, Redshift, or Databricks)
Fluency in a modern BI tool and the ability to build a semantic model, not just individual charts; Omni experience a plus
Understanding of ecommerce and DTC unit economics: contribution margin, CAC payback, cohort retention, and sell-through
Working knowledge of digital attribution and its limits, with the ability to reconcile platform-reported conversions against order data and explain the difference
Rigorous validation habits: checks date windows, denominators, and channel scope before reporting a number
Clearly separates what is observed from what is inferred, and speaks up when the data won't support a conclusion
Clear communication to non-technical stakeholders
Self-directed and comfortable as one of the first dedicated data hires, building without an established playbook

Nice To Haves

Apparel, fashion, or lifestyle brand experience; exposure to retail/POS or wholesale data
Python for analysis and automation
Demonstrated working habit with AI tools for analysis, code, and documentation
Exposure to reverse ETL, customer data platforms, and pushing audiences and attributes back into marketing tools
Experiment design and measurement: A/B tests, holdouts, and statistical significance

Responsibilities

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
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
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
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
Merchandising and inventory analysis: sell-through, size curves, markdown impact, and demand signals for planning
Design and measure experiments across site tests, campaign tests, and product launches, and report results with stated confidence
Partner with Finance on forecasting, unit economics, and channel profitability