Sr 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. Mid American Energy Company has an exciting career opportunity available. Take the next step in your career and apply now!

Bachelor's degree in information systems, computer science or related technical field or equivalent work experience. (Typically four years of additional related, progressive work experience would be needed for candidates applying for this position who do not possess a bachelor's degree.)

Eight or more years of experience with advanced knowledge of data architecture, cloud platforms (especially Azure), and enterprise data solutions.

Advanced proficiency with data engineering platforms and tools, particularly Azure Data Factory and Azure Databricks.

Advanced knowledge of core data engineering practices, including data modeling, ETL/ELT pipeline development, and performance tuning for enterprise-scale applications.

Experience across the data technology lifecycle, including solution design, development, optimization, administration, and licensing considerations.

Prior experience in the utility industry, with exposure to relevant data domains and operational environments.

Architect, Design, and Deliver Scalable Data Pipelines

* Lead the design and implementation of scalable ingestion and transformation frameworks on Azure, enabling efficient processing of structured, semi-structured, and unstructured data across enterprise platforms.

* Build, standardize, and maintain robust ETL/ELT pipelines using Azure Data Factory and Azure Databricks, including reusable patterns, error handling, and automated testing.

* Own complex integrations across on-premises systems, cloud storage, APIs, and streaming platforms, ensuring reliability, scalability, and clear interface contracts.

Lead Databricks Engineering and Platform Optimization

* Develop, review, and optimize Databricks notebooks and workflows using PySpark and SQL; establish engineering standards for readability, maintainability, and reuse.

* Implement and govern Delta Lake patterns for efficient storage, versioning, and ACID transactions, including retention, compaction, and schema evolution strategies.

* Leverage and administer Databricks capabilities (Unity Catalog, job orchestration, cluster policies) to balance security, performance, and cost across environments.

Define Data Architecture, Modeling Standards, and Lakehouse Patterns

* Design and evolve enterprise data models (star/snowflake and lakehouse-oriented models) to support analytics, reporting, and self-service consumption.

* Partner with data/solution architects to define lakehouse architecture, reference patterns, and design reviews that improve scalability, resilience, and maintainability.

* Lead implementation and optimization of Medallion Architecture (Bronze/Silver/Gold), defining SLAs, data contracts, and layering conventions for scalable, governed processing.

Establish Data Quality, Observability, and Governance Controls

* Implement automated data validation, profiling, and cleansing routines; define quality rules, thresholds, and exception workflows aligned to business-critical datasets.

* Ensure adherence to governance policies by implementing lineage, metadata, and cataloging practices; partner with governance stakeholders to close gaps and drive adoption.

Drive Performance Engineering, Monitoring, and Incident Resolution

* Monitor and optimize Spark jobs and data pipelines, applying performance and cost tuning (cluster sizing, partitioning, caching, and query optimization).

* Lead troubleshooting and root-cause analysis for latency, failures, and resource constraints; implement preventative fixes and improve runbooks/alerts to reduce recurrence.

Provide Technical Leadership and Stakeholder Partnership

* Partner with data scientists, analysts, and business stakeholders to shape data strategy, clarify requirements, and prioritize delivery based on value, risk, and dependencies.

* Translate business needs into durable technical designs (including data contracts and SLAs) and guide implementation to ensure solutions are scalable,…