[Hiring] 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 Addi’s marketing data platform, the pipelines and integrations that connect our data to Braze, AppsFlyer, GTM, Marketplace and the App.
• Use AI agents as the default way of working to make marketing data reliable, efficient and self-service.
• Ensure Marketing, Growth and Marketplace teams can launch campaigns and measure acquisition with data they trust.

What you will do

• Ramp up in 90 days:
  • Map the marketing data platform end to end (sources, jobs, tables, and integrations).
  • Write documentation for both people and AI agents.
  • Identify main pain points and share a prioritized roadmap.
• Reliable platform by month 3:
  • Add tests and alerts to all critical marketing pipelines.
  • Reach at least 99% on-time data for key tables and integrations.
  • Cut data incidents by 50% and find the root cause of data anomalies within 2 business days.
• Agentic automation by month 6:
  • Ship at least 2 AI agents or agentic workflows into production.
  • Handle recurring marketing data work with at least 3 active users.
  • Document improvements within 6 weeks of v1, removing at least 50% of today’s manual requests.
• Efficient platform by month 9:
  • Reduce the cost or runtime of the main marketing pipelines by at least 30%.
  • Remove unused or duplicated tables and jobs.
• Governance and documentation:
  • Keep one catalog of marketing data (tables, events, attributes) in the data catalog.
  • Include owners and approval rules for all data sent to marketing tools.

Qualifications

• Proven experience writing and debugging complex SQL without AI.
• Writes and reads joins at scale, window functions, CTEs and incremental logic with confidence.
• Can tell when a query result is wrong and explain why.
• Builds clean, reusable tables and datamarts that are easy to extend, test and document.
• Track record of building and operating ELT pipelines on a modern data platform.
• Hands-on with dbt, Airflow or similar, plus Git and code reviews.
• Has used SQL warehouses, notebooks and scheduled jobs.
• Uses tests, monitoring and alerts by default; comfortable with on-call and fixing issues at the root.
• Demonstrates ability to automate and integrate with Python.
• Builds scripts and API integrations that others can run without the author.
• Experienced in using coding agents daily for real data work.
• Writes a spec before building (inputs, outputs, transformations, checks).
• Can show a before and after.
• Has solid expertise in building agents and agentic workflows that others use.
• Understands failure modes, guardrails, and how to test an agent with real inputs.
• Demonstrated background in validating AI output.
• Has caught AI work that looked right but was wrong and added a check so it doesn’t happen again.
• Knows when human review is required, especially for customer data sent to marketing tools.
• Track record of writing documentation that people and agents can run on.
• Writes context and runbooks (READMEs, agent instructions, data catalog entries).
• Proven ability to translate business needs and own outcomes in ambiguity.
• Manages their own backlog and ships without close direction.
• Communicates clearly with technical and non-technical people; English is a must.

Desirable Qualifications

• Experience with marketing tools: CRM (Braze or similar), app attribution (AppsFlyer or similar), and tag management (GTM).
• Reverse ETL or CDP experience.
• Deeper data platform experience: advanced pipeline tools, Spark performance tuning, or a data platform certification.
• Experience with LLM APIs, LiteLLM, or building MCP servers.
• A/B testing and basic statistics.
• Knowledge of data privacy rules (consent, opt-out, Habeas Data).
• AWS and fintech or other regulated industry background.

Benefits

• Work on a problem that truly matters—redefining how people shop, pay, and bank in Colombia.
• Be part of something big from the ground up, influencing technology, strategy, culture, and values.
• Unparalleled growth opportunity in a fast-scaling company.
• Join a world-class team focused on excellence, ownership, and collaboration.
• Competitive compensation, equity in the company, and benefits that support your growth.

How the hiring process looks like

• Step 1: People Interview (30 min) - A conversation with a recruiter or hiring manager.
• Step 2: Initial Interview (45-60 min) - An in-depth conversation with the hiring manager.
• Step 3: Take Home Challenge (5-6 days) - Complete a simple take-home challenge within a 1-week window.
• Step 4: Take Home Challenge Review (60 min) - Meet with a Data Scientist and the Data Science Lead.
• Step 5: Stakeholder Interview (30 min) - Meet with a cross-functional partner.
• Step 6: Co-Founder Interview - Final conversation with the Founder to align on expectations.