Staff Data Engineer
R
Robots & PencilsApplied AI Engineering
Bogota, Colombia / Argentina (Remote Friendly)Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSPythonSQLKafkaCI/CDScalaData modeling
Requirements
- 7+ years of professional data engineering experience, with experience leading complex data platform initiatives
- Strong system architecture background with expertise in distributed data systems
- Expert proficiency in Python, Scala, and SQL
- Deep expertise with cloud-native data platforms and enterprise data warehousing
- Strong expertise in data pipeline orchestration and processing
- Strong experience with streaming platforms and real-time data processing (e.g., Kafka, Kinesis, Pub/Sub)
- Strong data modeling expertise and experience with data transformation
- Strong experience with data quality, governance, and compliance frameworks
- Strong experience with container orchestration and CI/CD for data systems
- Strong experience building data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows
- Demonstrated leadership and technical mentoring experience across a team or organization
- Demonstrable, day-to-day usage and expert knowledge of AI-forward coding tools such as Claude and Cursor
Responsibilities
- Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes
- Build and optimize scalable data pipelines supporting batch and real-time processing
- Define and enforce data governance, quality standards, and compliance frameworks across the platform
- Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation
- Drive data platform modernization, optimizing for performance, cost, and scalability
- Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams
- Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows
- Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption
- Partner with leadership on data strategy, translating technical depth into decisions others can act on
- Mentor junior and mid-level engineers, helping them grow their craft, confidence, and impact
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