Principal Data Architect

Electric Power Engineers • Austin, TX

1d • Remote

About The Position

We are designing the grid of the future! Be a part of an innovative team shaping the grid of the future through advanced energy intelligence. For more than half a century, Electric Power Engineers (EPE) has partnered with power and energy clients across the globe, providing consulting expertise and energy intelligence software solutions for complex engineering and grid modeling challenges. As leaders in the renewables space, we are focused on building a modern, secure, and resilient grid. Join us in making an impact on the communities we serve and the environment in which we live. Together we can transform the future of energy.

Join us in leading the change! We are designing the grid of the future. The Principal Data Architect will help establish EPE’s enterprise data architecture foundation, partnering across Software, Business Applications, Analytics, Finance, Cybersecurity, Infrastructure, and business teams to create trusted, scalable, and reusable data patterns. Reporting to the CIO, this role will be responsible for defining data architecture standards, integration patterns, canonical data models, curated data layers, and governance practices that support enterprise reporting, operational visibility, automation, analytics, and future AI use cases. This is a hands-on architecture role. The Principal Data Architect will serve as a senior technical resource who influences delivery teams, guides platform decisions, and helps shape the future enterprise data capability as EPE grows.

Requirements

• Bachelor’s degree in Information Technology, Computer Science, Data, Engineering, or a related field; master’s degree preferred.
• 10+ years of experience in data architecture, data engineering, enterprise applications, software engineering, analytics platforms, or related technology roles.
• Experience designing enterprise data models, data integration patterns, data pipelines, or modern data platforms.
• Experience with cloud data ecosystems, data lake/lakehouse patterns, data warehouses, or platforms such as Snowflake, Databricks, AWS, Azure, or similar technologies.
• Experience working across application, software, analytics, infrastructure, cybersecurity, and business teams.
• Experience supporting data governance, data quality, metadata, lineage, access control, or data classification practices.
• Strong understanding of modern data architecture, including lakehouse, warehouse, data mart, semantic layer, and curated data product patterns.
• Experience with data integration approaches such as ETL/ELT, APIs, streaming, batch processing, event-driven architecture, and service interfaces.
• Ability to define and document canonical models, data domains, schema standards, data contracts, and integration patterns.
• Knowledge of data governance, data stewardship, MDM, data quality, metadata, lineage, and data classification concepts.
• Understanding of security, privacy, compliance, and access control requirements for enterprise data environments.
• Ability to translate business problems into practical data architecture solutions.
• Ability to balance architectural standards, delivery speed, business value, and risk.
• Hands-on architect who can operate at both strategic and detailed levels.
• Strong communicator who can explain complex data concepts to technical and non-technical stakeholders.
• Collaborative partner who works well across Software, Business Applications, Analytics, Cybersecurity, Infrastructure, and business teams.
• Comfortable operating in a growing environment where data capabilities are still maturing.
• Practical and outcome-oriented, with the ability to create structure without unnecessary bureaucracy.
• Able to influence through expertise, credibility, documentation, standards, and partnership.
• Interested in helping build the foundation for a future enterprise data capability.

Nice To Haves

• Master's degree preferred.
• Experience enabling analytics, reporting, automation, AI/ML, GenAI, or RAG use cases through governed and reusable data patterns preferred.

Responsibilities

• Define and maintain EPE’s enterprise data architecture standards, including lakehouse, warehouse, data mart, and curated data layer patterns.
• Develop canonical data models, reusable data domains, and semantic definitions across key enterprise systems.
• Partner with application, software, and analytics teams to ensure data structures are scalable, consistent, secure, and maintainable.
• Help establish practical architecture patterns that support reporting, analytics, integration, automation, and AI readiness.
• Review proposed data models, pipelines, integrations, and platform changes for alignment with enterprise standards.
• Design and guide reliable data integration patterns across engineering data sets (PSS®E, PSCAD, ERCOT LMP, etc) and enterprise platforms (ERP, CRM, HR, project, finance, and operational systems).
• Define when to use APIs, event streams, batch interfaces, data pipelines, or platform-native integration patterns.
• Partner with software and engineering teams to expose data and system capabilities through secure, documented, versioned, and observable interfaces.
• Support the development of reusable data services and data products for approved downstream consumers.
• Help reduce duplication and fragmentation by promoting common integration, modeling, and documentation standards.
• Help establish EPE’s data governance foundation, including ownership, stewardship, metadata, lineage, classification, and quality expectations.
• Partner with business and technology stakeholders to define authoritative sources and trusted data definitions for key data domains.
• Create practical standards for data quality, data documentation, data lineage, and permitted use.
• Work with business teams to improve trust in enterprise reporting and analytics.
• Support governance forums and working groups that drive alignment, adoption, and accountability.
• Design data products and access patterns that support approved AI, GenAI, RAG, automation, and advanced analytics use cases.
• Ensure AI-related data capabilities are grounded in trusted sources, clear ownership, semantic definitions, metadata, lineage, and quality thresholds.
• Partner with Software, Analytics, Cybersecurity, and business teams to ensure AI use cases have secure and appropriate access to enterprise data.
• Help establish responsible data access practices for sensitive data, model inputs, and AI-enabled workflows.
• Design data architecture patterns that align with enterprise security, privacy, compliance, and access control requirements.
• Partner with Cybersecurity to support least-privilege access, role-based access, audit logging, credential management, and data-sharing controls.
• Help define observability expectations for data and integration services, including freshness, completeness, latency, failures, usage, access anomalies, and downstream impact.
• Support incident analysis related to data quality, data access, integration failures, or data misuse in partnership with the appropriate teams.
• Serve as a trusted advisor to business and technology teams on data architecture, integration, and governance decisions.
• Translate business needs into practical data architecture recommendations and delivery guidance.
• Influence teams through standards, design reviews, documentation, coaching, and clear decision-making.
• Help prioritize data architecture work based on business value, risk, complexity, and long-term maintainability.
• Contribute to the roadmap for EPE’s future enterprise data platform and data team structure.

Benefits

• Comprehensive health and wellness benefits including medical, dental, and vision with 100% premium coverage for you
• Generous PTO and paid holidays
• MyShare Employee Ownership Program
• Work with industry leaders
• 401K, up to a 4% match (100% vested from day 1)

What This Job Offers

• Job Type: Full-time
• Career Level: Principal