Data Analyst, Finance

Scale Media•Burbank, CA

3d•$80,000 - $100,000•Remote

About The Position

Scale Media is seeking a mid-level Data Analyst to support Finance by analyzing e-commerce financial data, maintaining forecasts, and ensuring revenue figures are accurate and traceable. The role connects raw transaction data, payment processors, financial reporting, and forecasts, helping Finance understand actual performance and what may come next. The analyst will build and maintain forecasts for revenue, orders, subscription renewals, and expenses by brand and channel; compare forecasts with actuals, explain variances, and update budget scenarios and assumptions. The role also covers financial and P&L reporting, unit economics, month-end close support, and budget-versus-actual analysis. A key focus is investigating and resolving financial discrepancies by reconciling payment processor data, bank deposits, internal records, and accounting data. The analyst will also reconcile marketing spend, document metric definitions, and work with Finance and the Data Engineer to address data issues at their source. For data preparation and reporting, the analyst will transform Snowflake data with SQL, apply financial business rules, and build and maintain dashboards in Sigma. Scale Media is a fully remote company focused on science-backed wellness brands and describes its culture as emphasizing ownership, candid feedback, thoughtful execution, communication, accountability, and trust.

Requirements

2–3 years of data analysis experience, ideally supporting finance; the role is intended for a hands-on mid-level analyst.
Hands-on experience building and maintaining financial forecasts, such as revenue, orders, or expenses, using SQL, spreadsheets, or a BI tool.
Comfort with revenue recognition, COGS, gross versus net sales, and contribution margin.
Proven DTC e-commerce experience, including familiarity with Shopify, Amazon Seller Central, and Stripe data.
Strong proficiency with Snowflake and SQL.
Advanced Excel or Google Sheets skills.
Ability to trace transactions through the data lifecycle and investigate revenue mismatches with patience and precision.
Ability to explain why actual results differ from forecasts.
Awareness of how ELT tools such as Hevo, Fivetran, Airbyte, or Azure function, sufficient to communicate effectively with a Data Engineer.

Nice To Haves

Experience with Sigma; candidates with Tableau, Looker, or Power BI experience may be able to adapt.
Competency in KNIME or dbt; experience with Alteryx or Python is also considered adaptable.
Understanding of QuickBooks report structures.

Responsibilities

Build and maintain forecasts for revenue, orders, subscription renewals, and expenses by brand and channel, updating forecasts as actuals arrive.
Compare forecasts with actuals, explain variances, and flag meaningful misses to the Finance team.
Update scenarios and assumptions used by Finance for budget planning.
Build and maintain reporting on revenue, gross and net sales, discounts, refunds, COGS, and contribution margin by brand, channel, and product.
Report on contribution margin, payback, and profitability by brand, channel, and product.
Support month-end close with timely, reconciled data.
Produce budget-versus-actual variance reporting and explain what changed and why.
Investigate revenue discrepancies by reconciling payment processor, bank deposit, internal database, and accounting data.
Reconcile marketing spend across advertising platforms and invoices against finance records.
Work with Finance and the Data Engineer to fix discrepancies at their source.
Document business logic behind financial metrics so teams use consistent definitions.
Prepare and transform data using KNIME, dbt, or similar tools such as Alteryx or Python/Pandas.
Write SQL to transform raw transaction data in Snowflake into datasets that reflect financial business rules.
Design, build, and maintain dashboards in Sigma for Finance and leadership.