Revenue Analytics Engineer (Full Stack) — Remote Poland

Affirm; Warszawa; Hybrid; PLN 226,000 - 346,000; Full time

Benefits offered by this job

Health coverage

Equity rewards

Flexible Spending Wallets for tech, 9

Away Days - wellness days

Learning & Development programs

Parental benefits

Employee Resource & Community Groups

Job summary

Affirm is seeking a Senior Analyst, Revenue Analytics to build scalable data products and end-to-end analytics powering the Revenue organization.

You will own data modeling, metric definitions, dashboards, automation, and enablement, while strengthening semantic layers and data governance to support reliable AI. Collaborate with engineering and business teams to translate questions into robust data infrastructure.

Qualifications

• 3+ years in analytics engineering or BI.
• Strong SQL, dbt, Python, data modeling, and data viz.
• Experience with BI tools and cloud data warehouses.
• Knowledge of AI data foundations, metadata and definitions.
• Ability to translate business questions into durable data products.

Responsibilities

• Develop dbt data models, dashboards, metrics, and automation processes for the revenue field team and revenue analysts.
• Build and maintain reporting data models that power external merchant reporting.
• Create semantic, metadata, and context layers for AI understanding of Revenue data.
• Partner with Business Systems, engineering, and stakeholders to translate requirements into data products.
• Contribute to GitHub-based workflows and release hygiene; scale analytics within Revenue.

Skills

SQL

dbt

Python

Data modeling

Data visualization

Tools

Sigma

Looker

Tableau

Databricks

Snowflake

Job description

Affirm is seeking a Senior Analyst, Revenue Analytics to build scalable data products and end-to-end analytics powering the Revenue organization.

You will own data modeling, metric definitions, dashboards, automation, and enablement, while strengthening semantic layers and data governance to support reliable AI. Collaborate with engineering and business teams to translate questions into robust data infrastructure.