Why Keyrus, Why Now

Keyrus is an international group of 3,000 experts across 30 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does.

For more than 30 years, we have been building the data foundations that make intelligent systems work, designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalize intelligence.

Intelligent systems are only as dependable as the data beneath them. Reliable pipelines, trusted data products, governed access, and meaningful enterprise context allow analytics, applications, models, and agents to operate consistently in production.

Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.

What you'll architect

As a Data Engineer, AI & Cloud, you will design, build, and maintain reliable data pipelines and data products that support analytics, enterprise applications, and intelligent systems.

This is primarily a Data Engineering role, with an additional focus on preparing enterprise data for AI applications. You'll work with structured and unstructured data, transforming information from different sources into trusted, reusable data products that support analytics, RAG, retrieval, evaluation, and agent-enabled systems.

You'll collaborate with AI Engineers, Software Engineers, Architects, and client teams to ensure data is accessible, reliable, and ready for production use.

Your responsibilities

- Design, build, and maintain production data pipelines, data models, and reusable data products that support analytics, enterprise applications, and AI solutions.




- Develop and optimize ETL/ELT processes, integrating structured and unstructured data from databases, APIs, files, and other enterprise sources.
- Implement data quality, testing, monitoring, lineage, and security practices to ensure reliable data in production.
- Prepare enterprise data for AI use cases, including RAG, retrieval systems, semantic context, and evaluation datasets.
- Manage and improve cloud-based data solutions, focusing on scalability, performance, reliability, and cost optimization.
- Collaborate with AI Engineers, Software Engineers, and Architects to deliver reliable data integrations and reusable engineering solutions.
- Work closely with clients to understand business needs, discuss data challenges, and deliver solutions aligned with project goals.

Who you are

You are a hands-on Data Engineer who understands that moving data is only part of the job. Data must also be reliable, well-organized, accessible, and ready to support business applications and intelligent systems.

You combine strong technical skills with practical problem-solving and enjoy collaborating with different teams to deliver reliable solutions.

What you bring

Experience

- Hands-on experience designing, developing, and maintaining production data pipelines, data models, and ETL/ELT processes.




- Experience working with cloud data platforms and building scalable data solutions.
- Experience implementing data quality, testing, monitoring, and security practices in production environments.
- Understanding of data engineering best practices, including performance, reliability, maintainability, and version control.
- Strong communication skills, with the ability to explain technical ideas clearly, collaborate with stakeholders, and build relationships with clients and internal teams.

Technical skills

- Strong SQL skills, including complex queries, data modeling, transformations, and validation.
- Proficiency in Python or another programming language used for production data engineering.
- Hands-on experience with at least one major cloud data platform, such as Databricks, Snowflake, Microsoft Fabric, BigQuery, or Redshift.
- Experience with data processing, orchestration, and transformation technologies such as Spark, dbt, Airflow, or comparable tools.
- Knowledge of data ingestion, APIs, batch processing, and data integration patterns.

Nice to have

- Experience preparing structured and unstructured data for AI/GenAI applications, including RAG, embeddings, retrieval, and evaluation datasets.
- Experience with vector databases, hybrid search, and maintaining retrieval indexes for AI applications.
- Experience with streaming, change data capture (CDC), or event-driven data architectures.
- Familiarity with semantic layers, knowledge graphs, feature stores, or MLOps integrations.
- Experience with CI/CD, infrastructure automation, rapid prototyping, or BI dashboard

📌 Data Engineer, AI (Colombia)
🏢 Keyrus Colombia
📍 Colombia