Data Analytics Engineer IV. 4P/796

4P Consulting•Atlanta, GA

6d•Hybrid

About The Position

Georgia Power is seeking an experienced Data Analytics Engineer IV to design, build, and support scalable data platforms, enterprise data pipelines, application integrations, and analytics solutions for Power Delivery. This role will work with Databricks, Azure Data Factory, Kafka, APIs, Microsoft Power Platform, OutSystems, and cloud-based lakehouse technologies to deliver trusted, governed, and reusable data products.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related technical field.
3–7 years of experience in data engineering, analytics engineering, or software development.
Experience developing enterprise data pipelines and cloud-based integrations.
Strong experience with ETL/ELT, data orchestration, data warehousing, data lakes, and lakehouse architectures.
Hands-on experience with Databricks, PySpark, Spark SQL, Delta Lake, and Unity Catalog.
Proficiency in Python and SQL.
Experience with Azure Data Factory, APIs, Kafka, CDC technologies, and SQL Server.
Experience with GitHub and Azure DevOps.
Experience with Power Apps, Power Automate, Power Platform, or OutSystems.
Strong analytical, communication, collaboration, and problem-solving skills.

Nice To Haves

Utility or Power Delivery industry experience.
Experience supporting transmission or distribution analytics.
Real-time data-streaming experience.
Knowledge of AI and machine-learning enablement using Databricks.
Experience integrating Power BI with Databricks, including DirectQuery architectures.

Responsibilities

Design, develop, and maintain enterprise ETL and ELT data pipelines.
Build ingestion frameworks for structured, semi-structured, batch, and streaming data.
Develop Databricks notebooks, workflows, pipelines, and reusable data products.
Ingest data using Azure Data Factory, Kafka, CDC technologies, APIs, files, and databases.
Build and maintain Bronze, Silver, and Gold data layers within the Databricks Lakehouse.
Develop solutions using PySpark, Spark SQL, Python, SQL, Delta Lake, and Unity Catalog.
Implement data-quality checks, monitoring, exception handling, lineage, and metadata.
Optimize lakehouse performance, scalability, and cloud costs.
Build and enhance business applications using Power Apps, Power Automate, Power Platform, and OutSystems.
Integrate Databricks data products with business applications and operational workflows.
Implement CI/CD practices using GitHub and Azure DevOps.
Support architecture reviews, solution design, governance, security, and documentation.
Partner with business users, analysts, developers, and technology teams.