- Location
- ATLANTA, GA
- Workplace
- Hybrid
- Type
- Contract
- Department
- Engineering
- Source
- RecruiterFlow
Description
Data Engineer IV – Modern Enterprise / Lakehouse / AI-Assisted
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
Skills
CI/CDSQLSQL ServerSparkAirflowDatabricksData EngineeringETLGitPower BI
