Data Engineer

Sunwest Bank•Draper, UT

1d•Onsite

About The Position

The Data Engineer designs, builds, and optimizes Sunwest Bank’s Azure‑based data infrastructure to support advanced analytics, AI‑driven workflows, and enterprise reporting. This role focuses on developing secure, scalable, and high‑performance data pipelines across Microsoft Azure services, including Blob Storage, Microsoft Fabric, and OneLake. The Data Engineer plays a critical role in enabling AI‑powered document processing by orchestrating ingestion, tagging, extraction, and integration of structured and unstructured data.

Requirements

5–10 years of experience in data engineering with a proven ability to build and maintain scalable data pipelines and ETL/ELT processes.
Hands‑on experience with Microsoft Azure services, particularly Blob Storage, Microsoft Fabric, and OneLake (or similar lakehouse platforms).
Strong development skills using Python, PySpark, and SQL.
Experience with orchestration tools such as Apache Airflow, Azure Data Factory, Databricks, or Apache Spark.
Experience integrating AI/ML services (e.g., OCR, NLP, Azure Cognitive Services) for document processing and insight extraction.
Strong knowledge of relational and NoSQL databases, including schema design and performance optimization.

Nice To Haves

Direct experience with Microsoft Fabric and OneLake; familiarity with Azure Synapse or Snowflake is beneficial.
Background in unstructured document processing (PDFs, images, text) using OCR and text‑extraction tools.
Understanding of NLP or AI/ML techniques for document classification and entity extraction.
Experience with data governance, metadata management, and security controls—particularly in regulated environments.
Comfort collaborating with data scientists and engineers to deploy AI models; familiarity with Agile methodologies and version control tools.

Responsibilities

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