Data Engineer IV- 4P/619

Experience Level: 5+ Years
Work Model: On-Prem + Cloud Hybrid Environments

Location- Atlanta, GA

Client- Georgia Power

Position Overview

The Data Engineer IV is a modern enterprise data engineering professional responsible for building, optimizing, and maintaining scalable data platforms in a hybrid on-premises and cloud environment. This role supports enterprise analytics, reporting, and AI-driven initiatives using a Lakehouse architecture, with Databricks as the strategic future-state platform.

The ideal candidate combines strong SQL and data modeling expertise with modern Spark-based technologies and familiarity with AI-assisted development tools.

Core Responsibilities

Enterprise Data Engineering

• Design, build, and maintain batch and/or streaming data pipelines
• Develop and optimize ETL processes using SSIS or similar tools
• Work with relational databases, data lakes, and NoSQL systems
• Normalize and model data using:
  • Star schema
  • Dimensional modeling techniques
• Transform raw data into curated, reusable datasets

Lakehouse & Modern Data Platforms

• Develop and support solutions on Spark-based platforms
• Work with Databricks Lakehouse architecture (primary future-state platform)
• Support analytics and reporting via Power BI
• Manage data orchestration workflows (e.g., Airflow or equivalent)
• Implement CI/CD and Git-based workflows for data pipelines

AI-Assisted & Modern Engineering Practices

• Leverage AI tools or copilots to assist with:
  • SQL development
  • Pipeline generation
  • Testing
  • Documentation
• Explore automation or AI agents to streamline engineering workflows

Technical Skills Required

Core Technologies

• Strong SQL and data modeling experience
• Hands-on experience with:
  • SQL Server
  • SSIS (or similar ETL tools)
  • Power BI
• Experience with Spark-based platforms
• Working knowledge of Databricks (preferred strategic platform)

Data Engineering Competencies

• Batch and real-time pipeline development
• Data quality and validation practices
• Relational and NoSQL systems
• Orchestration tools (Airflow or equivalent)
• CI/CD pipelines
• Git-based version control

Soft Skills & Work Environment Fit

• Strong written and verbal communication skills
• Comfortable collaborating with engineers and managers in:
  • Electric utility
  • Operations-heavy domains
• Able to operate in regulated, production-critical enterprise environments
• Analytical, detail-oriented, and solution-focused

Experience Requirements

• 5+ years in data engineering or related software engineering roles
• Experience in enterprise environments with hybrid on-prem and cloud systems