We are seeking a talented, self-directed Data Engineer to design, build, and operate large-scale, high-performance data infrastructure that powers analytics, AI/ML workloads, and intelligent automation across Device Operations. You will implement data structures using best practices in data modeling and ETL/ELT processes, build real-time and batch pipelines, and enable AI-ready data foundations that support both traditional BI and emerging agentic systems. You will gather business and functional requirements and translate them into robust, scalable solutions that work within the broader data architecture. You will analyze source systems, drive best practices with partner teams, and participate in the full development lifecycle - from design and implementation to documentation, delivery, and operational support.
The adecuado candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and is passionate about enabling data-driven decisions at scale. They are an expert in data modeling, ETL design,
and data warehousing - and are energized by the intersection of data engineering and AI/ML, where well-structured data infrastructure creates an outsized impact on intelligent systems. They are a self-starter, comfortable with ambiguity, able to think big while paying careful attention to detail, and thrive in a fast-paced, collaborative environment.
Key job responsibilities
- Design, implement, and operate scalable data pipelines (batch and real-time) that serve analytics, reporting, and AI/ML workloads
- Build and maintain data infrastructure that supports AI-ready datasets - structured for consumption by machine learning models, agents, and natural language interfaces
- Interface with technology teams to extract, transform, and load data from diverse sources using SQL, Python, and distributed computing frameworks
- Implement data models and ETL/ELT processes using best practices in dimensional modeling, data vault, or hybr
📌 Data Engineer II, DASH Device Operations (México)
🏢 Amazon
📍 México