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.

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, using AI agents as the default way of working, to make marketing data reliable, efficient and self-service, ensuring Marketing, Growth and Marketplace teams can launch campaigns and measure acquisition with data they trust..

What you will do

1. Ramp up in 90 days: Map the marketing data platform end to end (sources, jobs, tables, and integrations with Braze, AppsFlyer, GTM and Marketplace) by writing documentation that both people and AI agents can use, identifying the main pain points and sharing a prioritized roadmap.

2. Reliable platform by month 3: Add tests and alerts to all critical marketing pipelines to 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.

3. Agentic automation by month 6: Ship at least 2 AI agents or agentic workflows into production that handle recurring marketing data work (e.g., audience and segment requests, data quality triage, documentation upkeep), each with at least 3 active users, a runbook so others can operate it, and a documented improvement within 6 weeks of v1, together removing at least 50% of today’s manual requests to the Analytics Engineer.

4. Efficient platform by month 9: Reduce the cost or runtime of the main marketing pipelines by at least 30% by removing unused or duplicated tables and jobs.

5. Governance and documentation: Keep one catalog of marketing data (tables, events, attributes) in the data catalog, with owners and approval rules, so all data sent to marketing tools comes from official pipelines.

What we’re looking for

1. 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

   1. 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, and understands catalog, schema, table and permissions (advanced Spark tuning not required)

   • Uses tests, monitoring and alerts by default; comfortable with on-call and fixing issues at the root

   1. Demonstrates ability to automate and integrate with Python

   • Builds scripts and API integrations that others can run without the author

   1. Experienced in using coding agents daily for real data work

   • Uses Claude Code, Codex, Cursor or similar, not just autocomplete

   • Writes a spec before building (inputs, outputs, transformations, checks), delegates multi-step tasks to the agent and reviews the result

   • Can show a before and after

   1. Has solid expertise in building agents and agentic workflows that others use

   • Has built at least one agent or workflow that chains LLM calls with tools (SQL, APIs, MCP servers) and that other people actually use

   • Understands failure modes, guardrails, and how to test an agent with real inputs

   1. Demonstrated background in validating AI output

   • Has caught AI work that looked right but was wrong (e.g., a plausible SQL query with a bad join) and added a check so it doesn’t happen again

   • Knows when human review is required, especially for customer data sent to marketing tools

   1. Track record of writing documentation that people and agents can run on

   • Writes context and runbooks (READMEs, agent instructions, data catalog entries) that let a teammate or an AI agent maintain the system without the author

   1. Proven ability to translate business needs and own outcomes in ambiguity

   • Understands what Marketing, Growth and Marketplace need and decides whether the answer is a table, a pipeline, an agent or a self-service tool, instead of a one-off report

   • 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.

Why join us?

• Work on a problem that truly matters – We are redefining how people shop, pay, and bank in Colombia, breaking down financial barriers and empowering millions. Your work will directly impact customers' lives by creating more accessible, seamless, and fair financial services.

• Step 1: People Interview (30 min)
  A conversation with a recruiter or hiring manager to get to know you, your experience, and what you're looking for. We’ll also share more about Addi, our culture, and the role.

• Step 2: Initial Interview (45-60 min)
  A more in-depth conversation with the hiring manager, where we explore your skills, experience, and problem-solving approach. We want to understand how you think and work.

• Step 3: Take Home Challenge (5-6 days)
  Complete a simple take-home challenge within a 1-week window. With this technical challenge, we want to see your technical expertise solving a real-world problem. We expect that you invest 5 hours or less in developing a working solution.

• Step 4: Take Home Challenge Review (60 min)
  Meet with a Data Scientists and the Data Science Lead to talk about your take-home exercise submission and any questions you might have.

• Step 5: Stakeholder Interview: (30 min)
  Meet with cross-functional partner to talk about how you collaborate, communicate insights, and drive business impact, and to ask any questions about the team and its context.

• Step 6: Co-Founder Interview
  If there’s a strong match, you’ll have a final conversation with our Founder to align on expectations, cultural fit and ensure mutual excitement. From there, we’ll move quickly to an offer and discuss next steps.