Data Analyst (Remote U.S.)

About Scale Media

Scale Media builds and scales mission-driven health and wellness brands through data-driven storytelling and performance marketing. We are a fast-moving DTC and Amazon organization where creative testing, data insights, and consumer psychology come together to power performance.

About the Role - We are looking for a mid-level Data Analyst who is far more than a report builder. You are a "Data Detective" who understands the heartbeat of an e-commerce P&L.

You will not just analyze data; you will own the truth behind our Customer Acquisition. We need an Acquisition Data Master who understands the nuances of ad platforms (Google, Meta, TikTok, Amazon Ads) and is an expert in attribution, incrementality, and lift testing.

Additionally, you will act as a critical partner to our Finance team, bridging the gap between raw data, payment processors, and our financial reporting.

What You'll Do

1. Acquisition & Attribution Mastery

1. Deep-Dive Analytics: Ingest, clean, and analyze complex datasets from major ad networks (Meta, Google, TikTok, Amazon Ads) to provide granular insights on ROAS, CAC, and LTV.

1. Attribution Expert: Move beyond "last-click." Implement and refine multi-touch attribution models, run incrementality tests, and manage lift studies to determine the true value of our media spend.

1. Media Buying Support: Empower the growth team with actionable data on creative performance, audience segmentation, and channel efficiency.

2. Financial Data Integrity & Operations

1. Finance Support: Collaborate with the Finance team to support P&L analysis and assist in validating financial reports.

1. Reconciliation Investigations: Act as the lead investigator for revenue discrepancies. You will reconcile data between payment processors (Stripe/Shopify Payments/Amazon), bank deposits, and our internal database to ensure every dollar is accounted for.

1. Reporting: Generate reports that align operational metrics with financial outcomes (understanding QuickBooks report structures is a plus).

3. Data Transformation & Visualization

1. Data Preparation: While we have a Data Engineer managing the infrastructure, you will handle the "last mile" of data prep. You will use KNIME or dbt (or similar tools like Alteryx, Tableau Prep, or Python/Pandas) to clean, merge, and format data for specific analyses.

1. Business Logic Application: Write efficient SQL to transform raw log-level data in Snowflake into usable datasets that reflect our specific business rules (e.g., defining "new vs. returning" customers logic).

Dashboarding: Design, build, and maintain high-impact dashboards in Tableau. You will translate complex SQL queries into visual stories for stakeholders.

What Were Looking For

The Essentials (Experience & Tech Stack)

1. Experience Level: 2-3 years of specific Data Analysis experience. (We are not looking for Junior Analysts or Senior Data Architects; we need a hands-on mid-level executor).

1. Industry: Proven experience in DTC E-commerce is required. You must be fluent in Shopify and Amazon Seller Central data schemas.

1. Warehousing & BI: Strong proficiency with Snowflake (SQL) and Tableau (LOD expressions, complex visualizations).

1. Transformation Tools: Competency in KNIME or dbt is preferred, but we are open to candidates experienced with Alteryx, Prep, or Python who can adapt quickly.

1. ELT Familiarity: Awareness of how ELT tools (like Hevo, 5tran , Airbyte, Azure etc.) function, so you can communicate effectively with our Data Engineer when pipeline adjustments are needed.

The "Acquisition Master" Skill Set

1. You understand the difference between pixel data, server-side tracking, and ad-platform reported data.

1. You have executed or analyzed conversion lift tests and understand incrementality (preferably using a UMM tool like WorkMagic , Lifesight, Measured etc.)

1. You can explain the discrepancy between Facebook Ads Manager revenue and Shopify Analytics revenue without blinking.

The Financial Mindset

1. You are comfortable working with financial concepts (Revenue recognition, COGS, Net vs. Gross Sales).

1. You have the patience and precision to trace a single transaction through the entire data lifecycle to solve revenue mismatches.