Malbon

Direct-to-consumer golf apparel brand

Data Analyst / Engineer

Data Engineering +1Confirmed live in the last 24 hours

Full-timeSanta Monica, CA, USA

About the Job

Requirements

At least 4 years in a data analyst, analytics engineer, or business intelligence role, including meaningful experience at a direct-to-consumer or ecommerce brand.
A 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 or SMS platform.
Advanced SQL, including common table expressions, window functions, and query optimization against large tables.
Hands-on experience with a transformation framework such as dbt or equivalent and a cloud data warehouse such as Snowflake, BigQuery, Redshift, or Databricks.
Fluency in a modern business intelligence tool and ability to build a semantic model, not just individual charts.
Understanding of ecommerce and direct-to-consumer unit economics, including contribution margin, customer acquisition cost 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 differences.
Rigorous validation habits, including checking date windows, denominators, and channel scope before reporting a number.
Ability to distinguish observed from inferred findings and identify when data cannot support a conclusion.
Clear communication to non-technical stakeholders.
Self-direction and comfort building without an established playbook as one of the first dedicated data hires.

Responsibilities

Build and maintain pipelines that bring Shopify, advertising platform, Klaviyo, Google Analytics 4, retail and point-of-sale, and enterprise resource planning data into a central warehouse.
Design and own the transformation layer from staging through marts, including dimensional models, version control, and automated testing.
Own source-of-truth datasets used by other teams and define and document the business logic behind core metrics.
Use a Git-based analytics workflow to 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 key performance indicator definitions and reporting standards so the organization uses consistent metrics.
Deliver recurring daily trading, weekly performance, and monthly business-review reporting.
Reconcile revenue across web, point of sale, retail, apps, exchanges, and international channels.
Validate reported figures against source systems before they reach leadership, and investigate and resolve discrepancies.
Maintain documentation, data lineage, and metric definitions so the work is durable and reusable.
Identify repetitive data work suitable for artificial intelligence and automation, including reconciliation checks, documentation, and routine analysis, and put those workflows into production.
Flag when data cannot support a conclusion rather than producing an unsupported number.
Analyze channel efficiency, incrementality, and discrepancies between platform-reported and actual contribution.
Analyze customer cohorts, retention, repeat rate, lifetime value, and customer acquisition cost payback by acquisition source.
Analyze merchandising and inventory, including 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.

Desired Qualifications

Experience with an apparel, fashion, or lifestyle brand, and exposure to retail or point-of-sale or wholesale data.
Python for analysis and automation.
Demonstrated working habit with artificial intelligence tools for analysis, code, and documentation.
Exposure to reverse extract, transform, and load, customer data platforms, and pushing audiences and attributes back into marketing tools.
Experiment design and measurement, including A/B tests, holdouts, and statistical significance.
Omni experience with a modern business intelligence tool.

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
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.

About the Company

Malbon

Direct-to-consumer golf apparel brand

Malbon Golf is a lifestyle and apparel brand that fuses golf with streetwear, skate culture, and hip-hop influences. It operates primarily through direct-to-consumer sales on its website and wholesale partners, offering polos, sweatshirts, pants, outerwear, footwear, and accessories, while actively engaging in brand collaborations (e.g., TaylorMade, Beats by Dre, Girl Skateboards, Grateful Dead) to blend golf with broader cultural trends. Its products are designed to appeal to a younger, style-conscious audience and to make golf feel more relatable and

Company size: 51-200
Company stage: Early VC
Total funding: $2.1M
Headquarters: Santa Monica, CA
Founded: 2016

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