AI Analytics Engineer

About Addi

We are a leading financial platform, building the future of payments, shopping, and banking—a world where consumers and merchants can transact effortlessly and grow together. Today, we serve over 3.6 million customers and partner with more than 55,000 merchants, making Addi Colombia’s fastest-growing marketplace. With a state-of-the-art, technology-first approach, we provide banking solutions (deposits, payments, unsecured credit) and commerce services (e-commerce, marketing), bridging the financial gap for millions and redefining how people experience financial freedom. As the country’s leading Buy Now, Pay Later provider, we have secured regulatory approval to operate as a bank, unlocking even greater opportunities for our customers. In the past year, we have also achieved profitability, reinforcing the strength of our business model and our ability to scale sustainably. Our mission has earned the trust of world-class investors, including Andreessen Horowitz, Architect Capital, GIC, Goldman Sachs, Greycroft, Monashees, Notable Capital, Quona Capital, Union Square Ventures, Victory Park Capital, and more, who back our vision for the future. With their support, we are not just growing—we are transforming Latin America’s financial ecosystem and shaping the next generation to shop, pay, and bank in Colombia. But what truly sets us apart is how we build. We are a conscious company, driven by deep experience in scaling technology, services and products, and we live by our values every day.

About the Role

This is where you come in. Below, you’ll find what this role is all about—the impact you’ll drive, the challenges you’ll tackle, and what it takes to thrive at Addi. If you’re ready to be part of something big, keep reading.

What’s the mission you’ll drive

Own the end-to-end delivery of AI agents for the embedded function from problem identification through production adoption ensuring each shipped agent delivers measurable business impact and raises the team's execution standard for what AI can do.

What you will do

Ship AI Agents to Production: Identify high-friction workflows within the embedded team and iterate through testing to reach a stable deployment with active adopters and documented, quantified impact like time saved, cost reduced, or revenue generated.

Build a Self-Serve Operations Model: Create end-to-end documentation for each shipped agent covering inputs, outputs, failure modes, and resolution steps shortly after launch, enabling any teammate to run or maintain it independently.

Produce a Prioritized Opportunity Backlog: Conduct structured discovery sessions with key stakeholders in the embedded team to score problems by friction level, feasibility, and estimated impact, delivered as a living document reviewed regularly with the Champion.

Continuous Improvement: Continuously monitor the performance of shipped agents, identify opportunities for enhancement based on real-world usage and observed failure modes, implement documented improvements shortly after launch, and validate measurable gains in reliability, adoption, or output quality.

What we’re looking for

Proven experience in SQL and data modeling — accelerated with AI

Experience designing data models that AI agents can understand, maintain, and extend.

Strong SQL skills for querying and transforming large datasets, using AI to accelerate development, validate logic, and document transformations.

Experience applying AI-assisted testing and data quality validation as part of the standard development workflow.

Demonstrated experience with Python for building AI-powered data workflows

Experience building Python applications and automations that connect AI agents with data systems.

Track record of delivering Python-based pipelines or AI agents that can run reliably in production.

Track record of building data pipelines spec-first, with AI as the primary execution layer

Experience defining clear specifications for inputs, outputs, transformations, and quality checks before implementation.

Comfortable using tools such as Claude Code, LiteLLM, or similar AI development tools as part of the daily engineering workflow.

Ability to demonstrate how AI has significantly accelerated the delivery of production-ready data pipelines.

Experienced with ELT/ETL tools and AI orchestration