At Intellias , we're always on the lookout for exceptional talent to join our team in Colombia. Over 20 years of market experience, Intellias brings together technologists, creators and innovators in Europe, North and Latin America, and the Middle East. Join our international team and take the mission to solve the advanced tech challenges of tomorrow!

If you're enthusiastic about contributing your skills and experience to our organization, we invite you to submit your CV for this position.

What we offer you in Colombia

- Indefinite contract with Intellias Colombia SAS, payment in COP
- Health, life, funerary and pet insurance for the employee
- Extra vacation days
- Workation up to 90 day outside Colombia
- 100% work from home + internet subsidy
- 100% English environment

Must to have

- 8+ years of professional software and data engineering experience , including at least 3 years working with large-scale distributed data systems.
- Deep expertise designing and building scalable data platforms, pipelines, and distributed processing systems .
- Strong hands-on proficiency in Python , including production experience with technologies and libraries such as PySpark, Pandas, and FastAPI.
- Strong experience with Apache Spark/PySpark and distributed data processing at scale.
- Experience designing real-time and batch data pipelines using technologies such as Kafka, Spark, or comparable distributed processing platforms.
- Strong understanding of data architecture, data modeling, schema evolution, data quality, and heterogeneous data integration .
- Experience with workflow orchestration technologies such as Prefect, Apache Airflow , or equivalent.
- Experience building production backend services and APIs supporting data and platform capabilities.
- Hands-on experience with SQL and modern data storage technologies, including relational and NoSQL systems such as MongoDB .
- Strong understanding of distributed systems principles , including scalability, reliability, fault tolerance, consistency, and performance.




- Experience designing and implementing observability, logging, monitoring, and alerting for distributed data systems.
- Strong cloud-native engineering experience, including Docker, Kubernetes, CI/CD , and production deployment practices.
- Proven ability to independently design solutions for ambiguous, large-scale engineering problems spanning multiple systems and teams.
- Demonstrated experience establishing technical standards, engineering patterns, development practices, and reusable platform capabilities .
- Strong technical leadership skills with experience mentoring senior engineers, conducting design reviews, and influencing architecture without relying on formal people-management authority.
- Strong understanding of security, privacy, and data protection principles for production data platforms.
- Professional English sufficient for direct collaboration with U.S.-based engineering, product, ML, and platform teams.

Nice to Have

- Experience with FHIR, HL7, C-CDA , or other healthcare interoperability standards.
- Previous experience in healthcare, HealthTech, or another regulated data environment.
- Understanding of HIPAA, HITECH , or comparable data privacy and compliance requirements.
- Experience deploying or supporting LLMs, ML models, AI-enabled data products, or retrieval-based systems .
- Experience building data infrastructure supporting ML/AI workloads and production inference.
- Hands-on experience with observability technologies such as OpenTelemetry, Datadog, Prometheus , or similar platforms.
- Experience with DuckDB or modern analytical data-processing technologies.
- Experience with AI-assisted engineering tools such as Claude Code, GitHub Copilot, Cursor,



or comparable solutions.
- Contributions to open-source projects or publicly available engineering initiatives.
- Responsibilities:
- Design and implement solutions for large-scale, ambiguous data engineering and distributed-systems challenges .
- Architect, build, and evolve real-time and batch data infrastructure supporting operational, analytical, and AI/ML workloads.
- Design and scale event-driven data pipelines using technologies such as Kafka, Spark/PySpark , and modern data-processing platforms.
- Build backend services and APIs using Python, FastAPI , and cloud-native technologies to support product and platform capabilities.
- Define scalable approaches to data ingestion, transformation, modeling, validation, storage, and delivery.
- Establish and champion technical standards, architectural patterns, engineering best practices, and reusable platform components .
- Serve as a cross-team technical authority for data systems, pipelines, distributed processing, and platform architecture.
- Improve reliability, scalability, performance, and cost efficiency across data infrastructure.
- Implement robust observability, logging, monitoring, and alerting across distributed data systems.
- Design solutions with security, privacy, healthcare compliance, and scalability built in from the beginning.
- Improve engineering workflows, CI/CD pipelines, automated testing, and production deployment practices.
- Collaborate closely with Product, ML/AI, Platform, Infrastructure, and application engineering teams to build shared data capabilities.
- Provide technical mentorship to engineers through architecture reviews, design discussions, pairing, and knowledge sharing.
- Evaluate new data and AI technologies and introduce them where they provide measurable improvements to platform capabilities or engineering efficiency.
- Remain hands-on with production engineering , validating architectural decisions through implementation and operational experience.

📌 Staff Data Engineer (Colombia)
🏢 Intellias
📍 Colombia