[Hiring] AI Analytics Engineer @Addi

Role Description

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.

Qualifications

• 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.
• Hands-on experience with dbt, Airflow, or equivalent orchestration frameworks.
• Experience integrating AI capabilities into production pipelines, such as intelligent transformations, anomaly detection, or workflow automation.
• Ability to own data pipelines end to end, including the AI orchestration layer.
• Proven experience with data warehouses and AI-native querying.
• Strong experience working with BigQuery, Snowflake, Redshift, or Databricks at production scale.
• Experience using AI to optimize queries, improve documentation, and automate monitoring.
• Demonstrated ability to translate business problems into AI-powered data solutions.
• Ability to identify the right solution for a business problem, whether that is a pipeline, an AI agent, a model, or a combination of them.
• Experience building systems that automate recurring analytical work instead of relying on manual reporting.
• Track record of documentation and stewardship designed for AI replication.
• Experience producing documentation that enables both engineers and AI agents to maintain and extend data systems.
• Experience shipping data products with AI-assisted monitoring and operational visibility built in.
• Demonstrated ability to ship fast and iterate in ambiguous environments.
• Proven ability to deliver production-ready data solutions quickly using AI-first engineering practices.
• Comfortable working autonomously, prioritizing effectively, and delivering high-quality solutions with minimal oversight.

Benefits

• Work on a problem that truly matters – redefining how people shop, pay, and bank in Colombia, breaking down financial barriers and empowering millions.
• Be part of something big from the ground up – help shape a company, influencing everything from our technology and strategy to our culture and values.
• Unparalleled growth opportunity – the market we’re tackling is massive, and we’re growing faster than almost any fintech lender at our stage.
• Join a world-class team – work alongside top-tier talent from around the world, in an environment where excellence, ownership, and collaboration are at the core of everything we do.
• Competitive compensation & meaningful ownership – receive a generous salary, equity in the company, and benefits that go beyond the basics to support your growth.

How the hiring process looks like

• Step 1: People Interview (30 min) - A conversation with a recruiter to get to know you, your experience, and what you're looking for.
• Step 2: AI System Design Interview (60 min) - A dedicated deep dive into your AI systems thinking.
• Step 3: Take-Home AI Exercise - A hands-on exercise focused on building a simple, working AI prototype for a sample workflow.
• Step 4: On-site Interview (60 min) - A deep dive into your Take-Home AI Exercise to review your approach and talk through your implementation.
• Step 5: Stakeholder Interview (30 min) - A strategic discussion focused on your overall fit and ownership mindset.
• Step 6: Co-Founder Interview (30 min) - Meet a member of our leadership team to talk about Addi’s values, culture, and mission.