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Addi is a leading fintech platform in Colombia, serving over 2 million customers and 20,000+ merchants. The company provides banking solutions (deposits, payments, unsecured credit) and commerce services, operating as a Buy Now, Pay Later provider with recent regulatory approval to operate as a bank. Backed by top-tier investors including Andreessen Horowitz, Goldman Sachs, and Union Square Ventures, Addi achieved profitability last year and is transforming Latin America's financial ecosystem.
As an AI Analytics Engineer, you will own the end-to-end delivery of AI agents for the embedded finance function, from problem identification through production adoption. Your mission is to ensure each shipped agent delivers measurable business impact while raising execution standards for what AI can accomplish within the organization.
Key responsibilities include: shipping AI agents to production by identifying high-friction workflows and iterating through testing to reach stable deployment with active adopters and quantified impact (time saved, cost reduced, revenue generated); building a self-serve operations model with comprehensive documentation covering inputs, outputs, failure modes, and resolution steps; conducting structured discovery sessions with stakeholders to create a prioritized opportunity backlog scored by friction, feasibility, and impact; and continuously monitoring agent performance to identify enhancement opportunities and validate measurable gains in reliability, adoption, or output quality.
You will need proven expertise in SQL and data modeling accelerated with AI, including designing data models that AI agents can understand and maintain. Strong Python skills are essential for building AI-powered data workflows and production-ready applications. You should have a track record of building data pipelines spec-first with AI as the primary execution layer, using tools like Claude Code or LiteLLM. Hands-on experience with ELT/ETL tools (dbt, Airflow) and AI orchestration is required, along with production-scale experience in data warehouses (BigQuery, Snowflake, Redshift, Databricks). You must demonstrate the ability to translate business problems into AI-powered data solutions and ship fast in ambiguous environments, with documentation designed for both engineer and AI agent maintenance.
