Senior Data Engineer – AI Applications About the Role (NA/LATAM - FT)

Role Overview

Join our dynamic data and AI advisory team as a Senior Data Engineer. You'll play a crucial role in crafting production-grade solutions for enterprise clients, with an initial focus on supporting a healthcare real estate client. Your expertise will be pivotal in transforming complex structured and unstructured data into robust data platforms, analytics-ready datasets, and AI-enabled applications.

Responsibilities

• Data Engineering and Architecture: Design and implement reliable ETL and ELT pipelines for enterprise data, ensuring data is ingested, cleaned, classified, and transformed from various sources such as documents and APIs. Develop scalable data models and warehouse architectures to support analytics and AI applications.
• Backend and Application Development: Build APIs and backend services that connect data platforms with client-facing applications, and develop tools that allow users to interact with enterprise data.
• Applied AI and Unstructured Data: Construct data foundations for AI applications and develop workflows using client data to support AI agents capable of querying and analyzing information.
• Architecture and Reusability: Collaborate with technical leadership to define scalable architectures, create reusable components, and document technical decisions and workflows.

Required Skills

• 5+ years of experience in data engineering or software engineering.
• Proficiency in Python and advanced SQL skills.
• Experience with production ETL or ELT pipelines and designing cloud-based data warehouse solutions.
• Strong understanding of data quality, testing, monitoring, and error handling.
• Experience with backend services, APIs, and major cloud platforms like AWS, Azure, or GCP.
• Excellent communication skills and ability to work directly with clients.

Nice to Have

• Experience with LLM-powered applications, orchestration tools like Airflow, and data platforms such as Snowflake or BigQuery.
• Familiarity with vector databases, AI frameworks, and frontend technologies like React or TypeScript.
• Experience with Docker, CI/CD, and cloud deployment.
• Previous experience in consulting or client-services environments, especially in data-sensitive industries like healthcare or real estate.