AI Analytics Engineer
On-siteBogotá, Bogota D.C., Colombia
Job Summary
Own end-to-end delivery of AI agents for the embedded function, identifying high-friction workflows and iterating through testing to reach stable production deployments with documented, quantified business impact. Build a self-serve operations model by creating end-to-end documentation for each shipped agent covering inputs, outputs, failure modes, and resolution steps to enable independent maintenance. Produce a prioritized opportunity backlog through structured discovery sessions with key stakeholders to score problems by friction level, feasibility, and estimated impact. Continuously monitor agent performance, identify enhancements based on real-world usage, and implement documented improvements to validate gains in reliability and adoption.
Required Qualifications
- Proven experience in SQL and data modeling
- Experience designing data models that AI agents can understand, maintain, and extend
- Strong SQL skills for querying and transforming large datasets
- 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
- 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
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