Southern Company
Data Engineer
Birmingham, AL and 1 more · On-site · Senior
Sites: Birmingham, AL, Atlanta, GA
- Discipline
- Software
- Industry
- Energy
Spec sheet
Automatically summarized from the employer’s posting. Check the original description below for full requirements.
Data Engineer at Southern Company in Atlanta, GA or Birmingham, AL, building data pipelines for analytics and AI in multi-cloud environments. Requires 9+ years in data engineering, Python for AI/ML, Databricks, Spark, Azure data tools, and on-prem tools; leadership and collaboration expected.
Day shift · 9+ years · Bachelor’s degree in Computer Science, Engineering, Machine Learning, or related field
Required
- Databricks
- SQL
- Python
- Apache Spark
- data modeling
- Azure tools (Data Lake, Data Factory, Databricks, Synapse)
- Hadoop
- HIVE
Preferred
- Experience with on-prem tools (MSBI, Informatica)
- CI/CD
- Docker
- OpenShift
- data governance
- data quality controls
- leadership/mentorship
Benefits
- medical
- 401k
- PTO
Employer description
DATA ENGINEER
Locations: Atlanta, GA (30 Ivan Allen) OR Birmingham, AL (The Energy Center)
Onsite 4 says/week and one day remote
JOB SUMMARY:
The Electric Operations Technology Solutions (EOTS) Data Engineer is responsible for building, operationalizing, and maintaining data pipelines to support data, analytics, and AI use cases across hybrid and multi-cloud environments. The role involves translating data into consumable formats, supporting various data integration styles (e.g., virtualization, replication, streaming), and ensuring data reliability and scalability.
The data engineer will lead standardization of data curation, data taxonomy and solution delivery across the Electric Operations Technology Solutions Organization for the Databricks and Azure environments. The position is on the Electric Operations Technology Solutions Leadership Team engaging in strategic delivery of business solutions supporting multiple business areas within Southern Company.
Onsite 4 days per week/ 1 day per week remote
JOB REQUIREMENTS: (Education, Experience, Knowledge, Skills)
Experience and Knowledge:
Bachelor’s degree in Computer Science, Engineering, Machine Learning, or related field
9+ years of experience in data engineering or related roles
Experience with batch and real-time data processing frameworks
Experience with big data technologies (e.g., Hadoop, HIVE, Spark)
Experience with AI/ML solutions in Python
4+ years with on-prem tools (e.g., MSBI, Informatica, Oracle Golden Gate, SQL Server)
2+ years with Microsoft Azure tools (e.g., Data Lake, Data Factory, Databricks, Synapse, Power BI)
Experience with containerization (e.g., Docker, OpenShift)
Skills:
Proficiency in data modeling, access, storage, and quality toolsFamiliarity with Agile, DevOps, CI/CD methodologiesStrong communication and collaboration skillsAbility to lead and mentor othersFamiliarity with performance optimization and data governanceProactive in sharing knowledge and best practices
Hands-on expertise with Databricks, SQL, Python, and Apache Spark.
Proficiency in data modeling for analytics and machine learning.
MAJOR JOB RESPONSIBILITIES: Specify major duties performed by the job and % of time spent on duties.
Design and develop methods to process structured, semi-structured, and unstructured data using batch and real-time techniques
Develop release pipelines to automate build, version control, and deployment tasksBuild and maintain reusable templates, code libraries, and semantic modelsEstablish gatekeeping and guard rail processes for production data promotionCollaborate with cross-functional teams including data, business, and technical stakeholdersPromote data reusability and modular data product deliveryDefine and enforce data quality and integrity controlsProvide technical guidance to less experienced data engineersOptimize performance using parallel processing and minimal data movementStay current with data technologies and share insights across the organizationStandardize, design, and develop Databricks applications, data integrations and automated business processes.
Integrate data from multiple systems, databases, APIs, and business applications.
Provide technical guidance, knowledge sharing, and support for business users and fellow data engineers.
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