Minimum 10 years of progressive experience in data architecture, data engineering, cloud architecture, or related technical disciplines Minimum 7 years of hands-on experience designing, deploying, and operating data and AI workloads on AWS in enterprise environments Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience Demonstrated real-world experience designing and delivering enterprise data platforms, including data lakes, data warehouses, streaming architectures, and data governance frameworks Deep expertise in AWS data and AI platform services: Redshift, Glue, Lake Formation, Kinesis, EMR, Athena, SageMaker, Bedrock, and related ecosystem services Proven hands-on experience with Databricks or Snowflake deployed on AWS in enterprise environments Demonstrated experience designing AI-ready data architectures, including data preparation pipelines, vector stores, retrieval-augmented generation (RAG) patterns, and AI governance frameworks Proven ability to develop and articulate AI & Data business cases, connecting technical architecture decisions to client outcomes and measurable business value Experience authoring SOWs, LOEs, reference architectures, and delivery playbooks that technical teams can execute from Strong communication
Habilidades
, able to present data and AI architecture concepts to executive audiences and technical depth to delivery teams with equal effectiveness Strongly Desired Skills AWS Certifications AWS Certified Solutions Architect: Professional (strongly preferred) AWS Certified Data Engineer: Associate or Professional AWS Certified Machine Learning: Specialty Platform Certifications Databricks Certified Data Engineer (Associate or Professional) Snowflake SnowPro Core or Advanced: Data Engineer Technical Depth Enterprise data architecture patterns: data lakehouse, data mesh, data fabric, medallion architecture Data pipeline and orchestration tooling: Apache Airflow, AWS Step Fu...