Data Analyst / Engineer

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
• 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

What You'll Bring

• 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 Have

• 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

Compensation

This is a full-time position with an expected salary range of $120,000-$140,000. Compensation decisions are determined using a variety of job-related factors such as skill set, geographic location, market demands, experience, and education / certifications. If we extend an offer for employment, we will consider all individual qualifications.

Perks & Benefits

• Benefits: Competitive plans for Medical, Dental, Vision
• 401(k) + matching
• Annual RSU grant
• Generous PTO policy + observed holiday calendar
• Monthly mobile/internet stipend
• Employee discount for eligible Malbon products

Diversity & Inclusion

• At Malbon, we celebrate individuality.
• Our team is built on inclusivity, creativity, and community.
• We're committed to fostering a diverse workplace where every employee feels welcomed, respected, and empowered to grow.