Data Governance Engineer

Technology Omaha
14302 FNB Parkway
Omaha, NE 68154, USA

Tenaska is one of the largest privately held companies in the United States, an organization that’s adept in natural gas and power marketing, power management, development and acquisition of generation assets, operation of power plants and more.

Position Summary

Tenaska is seeking a Data Governance Engineer to help transform our enterprise data governance model from a traditional, manual approach into a modern, automated operating model powered by engineering practices and artificial intelligence.

This role will sit at the intersection of software engineering, data engineering, AI automation, and enterprise data management. The ideal candidate will build intelligent solutions that automate governance processes, improve data quality, strengthen metadata management, and enable teams across the organization to confidently leverage trusted data.

The Data Governance Engineer will play a key role in designing and implementing automated governance frameworks, developing AI-driven solutions, and partnering with technical and business stakeholders to create a scalable data ecosystem.

Essential Job Functions:

Automate Data Governance Processes

• Develop AI agents and workflow automation solutions using AI coding tools, large language models (LLMs), APIs, Model Context Protocol (MCP), and enterprise data sources.
• Design and implement automated governance controls directly within data pipelines to ensure trusted, compliant, and high-quality data reaches enterprise users and AI systems.
• Develop Policy-as-a-code functions using modern engineering practices, including version control, testing, and deployment processes.

Advance Metadata Management & Data Discovery

• Build and maintain automated metadata management frameworks to improve data cataloging, lineage visibility and data product management.
• Implement and enhance enterprise data catalog capabilities through active metadata management and automated discovery frameworks.

Improve Data Quality & Reliability

• Build and maintain automated data quality frameworks that proactively identify issues and anomalies.
• Implement monitoring, telemetry, alerting, and data drift detection capabilities.
• Evaluate and optimize data quality tools and processes to improve enterprise data trust.
• Partner with data engineering teams to optimize governance capabilities across modern cloud data platforms.
• Support administration and continuous improvement of enterprise data catalog, data quality, and master data management (MDM) platforms.
• Help define requirements and evaluate solutions that support enterprise data strategy.

Drive Data Governance Adoption

• Act as a bridge between technical teams, business stakeholders, data owners, data stewards, and risk/compliance partners.
• Translate complex governance concepts into clear, practical processes that enable adoption across the organization.
• Promote a culture of data ownership, accountability, and trust through self-service governance practices.

Required Qualifications:

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
• 4–6 years of experience in data engineering, software engineering, or a highly technical data management role.
• Experience designing, developing, and deploying production-grade software solutions.
• Strong programming experience with:
• Python
  • SQL
  • APIs
  • Version control tools (Git/Git Hub)
  • Software Development Life Cycle (SDLC) practices
• Experience working with modern data ecosystems, including cloud data platforms such as:
• Microsoft Fabric
• Snowflake
• Databricks
• Cloud data lakehouse and enterprise data warehouse architectures
• Experience developing automated data quality frameworks, controls, monitoring, and validation processes.
• Experience with metadata management, data cataloging, lineage, or data discovery solutions.
• Understanding of master data management (MDM) concepts and enterprise data governance practices.
• Experience collaborating with both technical teams and business stakeholders to drive data adoption and accountability.

Preferred Qualifications:

• Experience building AI agents or intelligent automation solutions using:
  • Large Language Models (LLMs)
  • Enterp…