Business Intelligence Engineer III

Summary

This role applies intermediate to advanced data engineering principles, theories, and concepts to solve a range of data and platform challenges of varying complexity. The Data Engineer III utilizes modern cloud data tools, distributed processing frameworks, and professional knowledge acquired through specialized training, education, and experience. The role exercises professional judgment—from limited to independent decision-making—within defined standards and practices to determine and execute appropriate technical actions.

The position develops practical expertise across data engineering domains including SQL-based transformation and modeling, cloud-native data platforms, and large-scale data processing using PySpark. The engineer acts as a collaborative and informed team member by providing technical analysis and recommendations in support of team and business outcomes, evaluating data engineering challenges and contributing to solution development through well-reasoned technical guidance.

Why This Role is Exciting

• Work with Leading Data Platforms: Build and support scalable cloud data solutions using Databricks, PySpark, SQL, and Azure-based services.
• Expand Your Data Engineering Expertise: Grow skills in distributed data processing, data modeling, automation, and CI/CD within a modern lakehouse architecture.
• Join an Innovative Team: Be part of a data engineering team with a strong history of delivering robust, reliable, and scalable data solutions that support enterprise analytics and decision-making.

Candidates must be willing to work in office at the Simplot HQ in Boise, ID. 

Key Responsibilities

• Participates as a member of a data engineering team responsible for cloud data platforms and pipelines
• Conducts thorough analysis of technical and data requirements ranging from moderate to advanced complexity to support solution design and implementation
• Designs, builds, and maintains data pipelines and transformations using SQL, Python, and PySpark
• Develops and supports data models and curated datasets optimized for analytical and downstream consumption
• Completes data engineering activities independently or as defined by more senior engineers, following established standards and best practices
• Leads or contributes to technical design reviews and outcome reviews
• Designs, prepares, and executes automated test plans for data pipelines and transformations
• Leverages advanced data engineering expertise to perform routine and complex maintenance, optimization, and production support, ensuring reliability, scalability, and performance
• Collaborates effectively with business, analytics, and technical teams, leveraging strong interpersonal skills to drive alignment and trust in data solution

Typical Education

Bachelor's Degree (B.A. or B.S.) from a 4-year college or university, preferably in Computer Science, Engineering, Information Systems, or a related field

Relevant Experience

5+ years of related experience and/or training

• • Advanced understanding of data engineering concepts with the ability to address complex data and platform challenges
  • Strong proficiency in SQL for data transformation, optimization, and modeling
  • Applies advanced data engineering methodologies including ELT/ETL, data orchestration, data modeling, and data quality practices
  • Familiarity with CI/CD practices and version-controlled deployments for data engineering solutions
  • Strong verbal and written communication skills, contributing to design discussions and technical documentation
  • Applies critical thinking and strong technical aptitude to solve complex data problems

Preferred Experience (Nice to Have)

• Hands-on experience with PySpark and Databricks for building and operating scalable data solutions
• Proficiency in Python for data engineering workflows and automation
• Experience working in Azure-based data platforms such as Databricks, Azure Data Lake, and Azure DevOps
• Experience delivering data solutions using Agile software development methodologies
• Experience supporting or enabling analytics, BI, or data science workloads