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.

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 hybrid approaches on MPP data warehouses

Build robust data integration pipelines using SQL, Python, and Spark across batch and streaming paradigms

Design and deliver high-quality datasets that support business analysis, customer reporting, and AI/ML feature engineering

Interface with business customers, gather requirements, and deliver complete, well-documented data solutions

Evaluate and make decisions around dataset designs, pipeline architectures, and tooling proposed by peer engineers

Produce comprehensive dataset documentation, metadata, and data lineage artifacts

Mentor junior da

📌 Data Engineer Ii, Dash Device Operations (Ciudad de México)
🏢 Amazon
📍 Ciudad de México