Data Analyst / Engineer

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

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

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.