Azure Data Engineer for AI-Driven Document Analytics

Sunwest Bank; Draper (UT); On-site; USD 110,000 - 170,000; Full time

Job summary

Sunwest Bank is seeking a Data Engineer to design, build, and optimize Azure-based data infrastructure for advanced analytics and AI-driven workflows. The role focuses on scalable pipelines, secure data handling, and enterprise reporting across Blob Storage, Fabric, and OneLake.

The engineer will implement ingestion, tagging, and processing pipelines, integrating AI/ML services for document processing and insights, while collaborating with data scientists and enterprise teams.

Qualifications

• 5–10 years of data engineering experience with scalable pipelines.
• Hands-on with Azure services (Blob, Fabric, OneLake) or similar lakehouse platforms.
• Strong Python, PySpark, and SQL development skills.

Responsibilities

• Design and optimize enterprise data architecture using Microsoft Azure technologies.
• Build and maintain scalable data pipelines across Azure Storage, Fabric, and OneLake.
• Develop orchestration engines for document ingestion, tagging, and processing workflows.
• Implement automated data extraction pipelines to derive structured insights from documents.
• Integrate AI/ML services with databases for combining document insights with enterprise data.
• Ensure data pipelines are secure, efficient, and optimized for AI‑driven applications.
• Support analytics and reporting through well‑structured data models and lakehouse architecture.

Skills

Azure data services

Python

PySpark

SQL

Orchestration tools

AI/ML integration

Data modeling

NoSQL databases

Tools

Azure Blob Storage

Microsoft Fabric

OneLake

Airflow

Azure Data Factory

Databricks

Job description

Sunwest Bank is seeking a Data Engineer to design, build, and optimize Azure-based data infrastructure for advanced analytics and AI-driven workflows. The role focuses on scalable pipelines, secure data handling, and enterprise reporting across Blob Storage, Fabric, and OneLake.

The engineer will implement ingestion, tagging, and processing pipelines, integrating AI/ML services for document processing and insights, while collaborating with data scientists and enterprise teams.