Staff Data Engineer
Robots and Pencils1 day ago
Remote, Argentina or Bogotá, ColombiaStaff+
Responsibilities
- Define data architecture and platform strategy across pipelines, warehouses, and data lakes.
- Build and optimize scalable batch and real-time data pipelines.
- Define and enforce data governance, quality, compliance, monitoring, logging, and alerting standards.
- Drive data platform modernization for performance, cost, scalability, reliability, and security.
- Design data contracts and event flows with backend, platform, and engineering teams.
- Lead data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training and inference data flows.
- Integrate data services with APIs, middleware, and third-party systems.
- Partner with leadership on data strategy and collaborate with engineering, analytics, AI, and product teams.
- Establish data engineering standards, make architectural decisions, and mentor junior and mid-level engineers.
Requirements
- 7+ years of professional data engineering experience and experience leading complex data platform initiatives.
- Strong systems architecture background and expertise in distributed data systems.
- Expert proficiency in Python, Scala, and SQL.
- Deep expertise with cloud-native data platforms, enterprise data warehousing, pipeline orchestration, and processing.
- Strong experience with streaming platforms and real-time processing such as Kafka, Kinesis, and Pub/Sub.
- Strong data modeling, data transformation, data quality, governance, and compliance experience.
- Strong experience with container orchestration and CI/CD for data systems.
- Experience building production AI/ML data pipelines, including embeddings, vector stores, RAG preparation, feature stores, and training and inference flows.
- Demonstrated technical leadership and mentoring experience across a team or organization.
- Strong stakeholder communication, problem-solving, judgment, and ability to navigate ambiguous technical and business challenges.
- Demonstrable daily use and expert knowledge of AI-forward coding tools such as Claude and Cursor.
- Experience with data mesh, data fabric, lakehouse architectures, or governance framework implementation is a plus.
Tech Stack
Categories
Data Engineering