Analytics Engineer

You'll own the accuracy, reliability, and scalability of financial data and reporting across all brands. Sitting at the intersection of finance, commercial, data, operations and engineering, you'll work closely with finance and commercial teams to gather requirements, and with engineering and operations teams globally to shape the data collected, producing best-in-class financial data models. You'll have full end-to-end ownership: implementation, governance, data quality monitoring, and documentation, directly supporting decision-making and capital allocation across markets.

Responsibilities

• Maintain and improve the accuracy and reliability of core orders, revenue & refunds models
• Configure and manage integrations with financial reporting tools, e.g. Netsuite
• Standardise reporting across each region (UK, EU, AU, JP, CA)
• Partner with engineering and operations to manage changes across products and processes, reducing downstream reporting impacts
• Work with the US Hims & Hers financial team to provide accurate, reliable and well documented data for SEC reporting
• Build automated checks for data reliability, and monitor drift of reporting figures
• Maintain a deep understanding of upstream source data to understand change impacts to reporting figures
• Model product COGs and services costs with revenue data to build payback models informing capital allocation decisions
• Work closely with finance, commercial and operations teams to maintain product COGs data
• Work with marketing teams to accurately track and attribute marketing spend data (Google, Facebook, TikTok, etc.)
• Monitor and validate data accuracy across financial source data
• Build automated checks for missing or duplicated data, schema drift, and anomalies
• Investigate discrepancies between source data and reported figures
• Maintain end to end documentation of reporting models

Requirements

• 4-6 years in an analytics engineering, financial or commercial analytics role
• Experience building / maintaining production data models
• Strong SQL skills, ideally in BigQuery, for data modelling and validation
• Detail orientated, able to self-validate assumptions and potential data gaps
• Strong sense of ownership
• Familiarity with dbt or similar transformation tools
• Familiarity with financial concepts, e.g. Revenue, CAC, Payback, LTV
• Strong documentation habits