Principal Data Engineer

Mid American Energy Company, a Midwest utility, provides regulated electric and natural gas service to more than 1.6 million customers in Illinois, Iowa, Nebraska and South Dakota. The company owns and operates a portfolio of power-generating assets, approximately 61% of which is wind generation. Mid American Energy Company is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or religious creed, age, national origin, ancestry, citizenship status (except as required by law), gender (including gender identity and expression), sex (including pregnancy), sexual orientation, genetic information, physical or mental disability, veteran or military status, familial or parental status, marital status or any other category protected by applicable local, state or U.S. federal law.

Employees must be able to perform the essential functions of the position, with or without an accommodation.

* Bachelor's degree in information systems, Computer Science, or a related technical field; or equivalent work experience.

* 10 years of experience with advanced knowledge of data architecture, cloud platforms (especially Azure), and enterprise data solutions.

* Advanced understanding of data modeling, ETL/ELT processes, and performance tuning of enterprise-level applications.

* Expert-level knowledge of data-related technologies from architecture to administration, including design, development, optimization, and licensing.

* Proven experience working in the utility industry is required

* Soft Skills:

* Effective oral and written communication skills, with the ability to collaborate across teams and mentor junior engineers.

* Strong analytical and problem-solving abilities.

* Ability to prioritize and manage multiple tasks and projects concurrently.

Primary

Job Duties and Responsibilities (Essential Job Functions)

* Design and Develop Scalable Data Pipelines

* Design and implement scalable data ingestion and transformation frameworks using Azure services enabling structured, semi-structured, and unstructured data to be efficiently processed and integrated into enterprise data platforms

* Build and maintain robust ETL/ELT pipelines using Azure Data Factory and Azure Databricks.

* Integrate data from diverse sources including on-premises systems, cloud storage, APIs, and streaming platforms.

* Databricks Development and Optimization

* Develop and optimize notebooks and workflows in Azure Databricks using PySpark, SQL.

* Implement Delta Lake for efficient data storage, versioning, and ACID transactions.

* Leverage Databricks features such as Unity Catalog and job orchestration.

* Data Modeling and Architecture

* Design and implement data models (star/snowflake schemas) for analytics and reporting.

* Collaborate with architects to define data lakehouse architecture and best practices.

* Hands-on experience implementing and optimizing data solutions using the Medallion Architecture (Bronze, Silver, Gold layers) for scalable and structured data processing

* Data Quality and Governance

* Implement data validation, profiling, and cleansing routines.

* Ensure compliance with data governance policies, including data lineage and metadata management.

* Performance Tuning and Monitoring

* Monitor and optimize performance of Spark jobs and data pipelines.

* Troubleshoot and resolve issues related to data latency, job failures, and resource utilization.

* Collaboration and Stakeholder Engagement

* Work closely with data scientists, analysts, and business units to understand data requirements.

* Translate business needs into technical solutions that are scalable and maintainable.

* Security and Compliance

* Implement role-based access control (RBAC), encryption, and secure data handling practices.

* Ensure compliance with industry regulations (e.g., NERC CIP, GDPR, HIPAA if applicable).

* Documentation and Best Practices

* Maintain clear documentation of data flows, architecture, and operational procedures.

* Promote best practices in code versioning, testing, and CI/CD for data engineering.