Data Engineer III

Location - Atlanta, GA 30308 / Birmingham, AL 35203

Contract - 1 Year

Client - Alabama Power

Position Overview

We are seeking an experienced Data Engineer (Level 3) to build, optimize, and maintain scalable data pipelines and datasets that support business-critical analytics and applications. This role sits outside traditional IT and operates within the business technology / data domain-focused on ensuring clean, structured, and accessible data assets.

The ideal candidate will have deep experience in data transformation, SQL optimization, Data Lakehouse architectures, and distributed processing systems. Proficiency in Databricks, Azure, Spark, Hive, and Airflow is required.

Key Responsibilities Data Engineering & Pipeline Development

Design, build, and maintain scalable and reliable data pipelines

Combine raw data from multiple sources into clean, structured, machine-readable datasets

Implement transformations using Spark, SQL, and ETL / ELT tools

Normalize and model relational and distributed datasets to support analytics & applications

Lakehouse & Cloud Engineering

Develop and optimize data architectures in Databricks Lakehouse & Azure

Manage ingestion pipelines across structured and unstructured data sources

Apply data governance, quality, and lineage methodologies in cloud environments

Data Modeling & Quality

Create logical and physical data models to support business intelligence, ML, and analytics workloads

Ensure data consistency, accuracy, completeness, and reusability

Establish data quality frameworks, validation checks, and monitoring automation

Experience

5 10 years in a Data Engineering role with hands-on development

Strong background in data manipulation & software engineering-oriented data practices

Experience supporting enterprise-scale platforms

Technical Skills

Advanced SQL (query optimization, stored procedures, tuning)

Databricks & Microsoft Azure Cloud (mandatory)

Spark / Hive / Airflow

Strong data modeling (star, snowflake, third-normal form)

Experience building ETL / ELT pipelines in distributed environments