[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![Affirm logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/fac7714c-221-1.jpg)](https://jobicy.com/company/affirm.md)Remote opportunity at[Affirm](https://jobicy.com/company/affirm.md) # Analyst II, Full Stack (Revenue Analytics) Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/affirm.md)Share14 Sep 2026Published35Listing views2Application actions14 Oct 2026Apply before Opportunity details ## About this role. AI SummaryAffirm is seeking an Analyst II for its Revenue Analytics team to build and own data products, reporting infrastructure, semantic layers, and analytical systems for the Revenue organization. The role combines analytics engineering and business intelligence work, including dbt modeling, SQL, Python, dashboards, metric definitions, automation, and data governance. This analyst will partner with engineering, Business Systems, and commercial stakeholders to turn ambiguous requirements into reliable, scalable data products. The position also emphasizes AI-ready data foundations, including metadata, documentation, semantic context, evaluations, and data quality. It is a remote role for candidates based in Spain. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 4/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThe role requires strong hands-on analytics engineering skills alongside ownership of critical reporting models and AI-ready semantic foundations. Ambiguous commercial questions, cross-functional coordination, and production-quality data practices make it more demanding than a dashboard-only analyst role. ## Salary analysis Estimated compensation compared with the broader EU market for similar roles. Estimated job medianBelow market€81,000EU market range€100k–€145k0€160k AI insightThe disclosed Spain base-pay range is €63,000–€99,000 per year, with a midpoint of €81,000 per year. For comparison, the estimated US market base-salary range for an Analyst II / analytics engineering-focused revenue analytics role with 3+ years of experience is approximately $100,000–$145,000 annually; this US market comparison is an estimate and excludes equity and benefits. ## Core skills Skills and capabilities most closely associated with this opportunity. [SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[data modeling](https://jobicy.com/jobs?search_keywords=data%20modeling.md)[business intelligence](https://jobicy.com/jobs?search_keywords=business%20intelligence.md)[data visualization](https://jobicy.com/jobs?search_keywords=data%20visualization.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[Databricks](https://jobicy.com/jobs?search_keywords=Databricks.md)[semantic layers](https://jobicy.com/jobs?search_keywords=semantic%20layers.md)[data governance](https://jobicy.com/jobs?search_keywords=data%20governance.md) Sample interview questionsHow would you design a trusted revenue metric used across dashboards, merchant reporting, and AI-assisted analysis?I would begin by aligning stakeholders on the business definition, grain, source systems, exclusions, and ownership. I would implement the metric in a version-controlled dbt model, add tests for freshness, uniqueness, referential integrity, and reconciliation, then document it in the semantic layer so all downstream consumers use the same governed definition. Describe your approach to converting an ambiguous stakeholder request into a scalable data product. I first clarify the decision the stakeholder needs to make, the intended users, success criteria, cadence, and acceptable latency. I then map the relevant source data, define the metric logic and model grain, deliver a small validated iteration, and evolve it into a documented, tested model and dashboard or automated workflow. What practices would you use to ensure dbt models supporting external merchant reporting are reliable? I would use layered modeling, clear ownership, code review, CI checks, source freshness monitoring, dbt tests, and reconciliation against authoritative systems. For externally visible reporting, I would also establish release procedures, monitor post-release changes, document known limitations, and provide an auditable lineage from source data to reported metrics. How can semantic layers and metadata improve the reliability of AI systems that use revenue data? They provide AI systems with governed business definitions, approved dimensions, lineage, access context, and examples of correct usage. This reduces ambiguity around terms such as revenue, merchant volume, or active accounts, while evaluations and data-quality controls help detect incorrect queries or unsupported conclusions. How would you communicate an analytical recommendation to both revenue leaders and engineering partners? For leaders, I would lead with the decision, the key insight, expected impact, assumptions, and recommended action using concise visuals. For engineering partners, I would provide the metric specification, source lineage, technical constraints, validation results, and implementation plan so they can assess and deliver the solution effectively. At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. We’re looking for a curious, driven professional to join our Revenue Analytics team. This builds and owns the data products, reporting infrastructure, semantic foundations, and analytical systems that power Affirm’s Revenue organization. As a Analyst at Revenue Analytics, you’ll build scalable data products that power day-to-day decision-making – owning end-to-end work across data modeling, metric definitions, dashboards, automation, and enablement. You’ll also help strengthen our semantic layer and data governance, laying the foundation for reliable AI. The ideal candidate combines strong technical and analytical skills with the ability to turn ambiguous business questions into durable, well-tested data infrastructure. What you’ll do * Develop dbt data models, dashboards, metrics, and automation processes for the revenue field team and revenue analysts * Build and maintain critical reporting data models that power external merchant reporting * Build the semantic, metadata, and context layers that allow AI systems to accurately understand Revenue data, metrics, and business definitions * Partner with Business Systems, engineering, and business stakeholders to translate requirements into durable, well-tested data products * Contribute to the team’s best practices in version control, code review, documentation, and release hygiene (GitHub-based workflows) * Develop processes, governance, and foundations to scale the impact of analytics within Revenue. What we look for * 3+ years of work experience in an analytics engineering or business intelligence role * Strong working knowledge of SQL, dbt, Python, data modeling, and data visualization * Hands-on experience with BI tools (Sigma/Looker/Tableau), Databricks, and cloud data warehouses (Snowflake) * Understanding of the data foundations required for reliable AI, including semantic layers, metadata, evals, metric definitions, documentation, and data quality * Demonstrated experience integrating AI tools into day-to-day analytics engineering workflows to improve development speed, quality, and scalability * Familiarity with Salesforce and experience supporting commercial areas of the business * Ability to identify user needs and translate them into robust, scalable data products * Ability to start with an ambiguous problem, deconstruct it into tangible steps, and work toward an impactful solution * Ability to communicate findings and recommendations clearly to both technical and non-technical audiences. ### Compensation and Benefits Base Pay Grade – K Equity Grade – 3 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). We are able to offer visa sponsorship for this role, but do require that someone is based in Spain for the role. ESP base pay range per year: 63,000 – 99,000 EUR Additional benefits include: * Type of employment: Contract of Employment * Flexible Spending Wallets for tech, food and lifestyle * Away Days – wellness days to take off work and recharge * Learning & Development programs * Parental benefits * Employee Resource & Community Groups This role is eligible for creative tax benefits, subject to applicable law and company policy Location – Remote Spain The majority of our roles can be located anywhere in Spain. **This job description is not a contractual document, and is not intended to have binding force.** #LI-Remote Remote-first with flexibility built in Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience. Benefits designed for you Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights: * Health coverage at no cost: We cover 100% of premiums for employees and their dependents. * Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you. * Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to. * Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount. We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help. For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law. By clicking “Submit Application,” you acknowledge that you have read Affirm’s [Global Candidate Privacy Notice](https://www.affirm.com/global-candidate-privacy-notice) and consent to the use of your personal information as described. 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