Data Analytics Engineer

• The Data Engineer is responsible for designing, building, and supporting scalable data platforms, data products, application integrations, and analytics solutions that enable business decision-making across Power Delivery.
• This role develops and maintains enterprise data pipelines, integrates operational systems with cloud-based lakehouse platforms, and supports both low-code and pro-code application development using Client Power Platform, OutSystems, and Databricks.
• The successful candidate will partner with business stakeholders, data analysts, application developers, and technology teams to deliver trusted, governed, and reusable data assets.

• Data Engineering & Integration Design, build, and support enterprise data pipelines using ETL and ELT methodologies.
• Develop scalable ingestion frameworks for structured, semi-structured, and streaming data.
• Build and maintain Databricks notebooks, workflows, and data pipelines.
• Ingest data from source systems using: Azure Data Factory Kafka CDC technologies APIs Files and database sources Implement data quality checks, monitoring, and exception handling. Support Bronze, Silver, and Gold data architectures within the Databricks Lakehouse.
• Databricks Development Design and maintain Delta Lake tables and data products.
• Develop solutions using: Databricks PySpark Spark SQL Python SQL Optimize lakehouse performance, scalability, and cost management. Support data sharing, governance, and catalog management using Unity Catalog.
• Application Development Build and enhance business applications using: Client Power Apps Power Automate Power Platform OutSystems Develop integrations between Databricks and business applications.
• Partner with users to automate workflows and operational processes.
• Data Governance & DevOps Follow Southern Company governance and security standards. Implement CI/CD practices using GitHub and Azure DevOps.
• Maintain documentation, lineage, and metadata for enterprise data assets.
• Participate in architecture reviews and solution design activities.

• Computer Science Information Systems Data Analytics Engineering Related technical discipline Experience 3-7 years of experience in data engineering, analytics engineering, or software development.
• Experience building enterprise data pipelines and integrations.
• Experience with cloud-based data platforms.

We are seeking a Data Analytics Engineer to build and support scalable data pipelines, lakehouse solutions, integrations, and analytics applications. The role partners with business, data, application, and technology teams to deliver governed and reusable data products.

Key Responsibilities

• Design, develop, and support ETL/ELT data pipelines and ingestion frameworks for structured, semi-structured, and streaming data.
• Build and maintain Databricks notebooks, workflows, Delta Lake tables, and data products using PySpark, Spark SQL, Python, and SQL.
• Implement Bronze/Silver/Gold (Medallion) Lakehouse architecture and optimize performance, scalability, and cost.
• Integrate data from Azure Data Factory, Kafka, CDC, APIs, files, databases, and SQL Server.
• Implement data quality, monitoring, exception handling, metadata, lineage, and governance.
• Use Unity Catalog for data governance, security, and catalog management.
• Develop business applications and workflow automation using Power Apps, Power Automate, Power Platform, and OutSystems.
• Build integrations between Databricks and business applications.
• Support CI/CD, GitHub, and Azure DevOps and participate in architecture/solution design.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or related technical field.
• 3–7 years of experience in data engineering, analytics engineering, or software development.
• Strong experience building enterprise data pipelines, integrations, and cloud-based data platforms.
• Hands-on experience with:
  • Databricks, PySpark, Spark SQL, Python, SQL
  • Delta Lake, Unity Catalog, Lakehouse/Medallion architecture
  • Azure Data Factory, Kafka, CDC, APIs
  • ETL/ELT, data warehousing, data ingestion, pipeline orchestration
  • GitHub, Azure DevOps
  • Power Apps, Power Automate, Power Platform, or OutSystems

Preferred

• Utility industry / Power Delivery, Transmission, or Distribution experience.
• Real-time/streaming data experience.
• AI/ML enablement on Databricks.
• Power BI and DirectQuery integration experience.