Job Description Summary

GE Vernova is the leading software provider for the operations of national and regional electricity grids worldwide. Our software solutions range from supporting electricity markets, enabling grid and network planning, to real-time electricity grid operations.
Data & AI are at the heart of the on-going digital transformation, shaping the future of grid operation, clean energy initiatives, and AI driven customer experiences.

Job Description

As a Technical Data Architect (Data and Analytics) specializing in electric utilities, you will play a pivotal role in defining and implementing solutions leveraging the GridOS Data Fabric, which is the grid data management layer of GE Vernova’s GridOS Platform.

You will act as the bridge between business users and technical teams, guiding the design, rollout, and adoption of a data fabric that supports data integration, governance, and accessibility across the organization

You will work closely with clients to understand their business needs, translate them into use cases that the technical team can design and build. Your role will involve collaborating with domain experts, business analysts, solution architects to ensure that the data fabric meets operational, regulatory, and strategic needs - especially in the context of CIM/CGMES, SCADA, metering, asset management and energy market integration.

Key Responsibilities:

Business alignment & Requirements

• Lead discussions with business users (IT and OT), subject matter experts (SMEs), and stakeholders to gather data needs and translate them into functional architecture components.

• Define domain-driven data models aligned with electric utility processes (grid operations, asset management, outage response, etc.).

• Drive workshops with customers capturing the foundation for a sucessful GridOS Data Fabric realization. Act as a thought leader within the utility domain, understanding and challenging the utilites data and business processes.

Solution Design

• Lead discussions with business users (IT and OT), subject matter experts (SMEs), and stakeholders to gather data needs and translate them into functional architecture components.

• Define domain-driven data models aligned with electric utility processes (grid operations, asset management, outage response, etc.).

• Drive workshops with customers capturing the foundation for a sucessful GridOS Data Fabric realization. Act as a thought leader within the utility domain, understanding and challenging the utilites data and business processes.

Energy Domain and Regulatory integration

• Align data architecture with TSO/DSO regulatory obligations (e.g., ENTSO-E).

• Integrate models like CIM (IEC 61970/61968) and CGMES into the data architecture.

• Address interoperability with grid modeling, EMS/SCADA systems, and market data platforms.

Governance & Enablement

• Define data ownership, stewardship, and usage policies in cooperation with Data Governance teams.

• Support data cataloging, lineage tracing, and business glossary implementation.

• Ensure that data is discoverable, trustworthy, and accessible for use by analytics and AI teams.

Collaboration & Delivery

• Train and support business users in self-service data access and semantic model navigation.

• Contribute to change management and adoption across internal stakeholders and external partners.

• Mentoring, training and knowledge sharing: Collaborate with the Services function (Delivery Manager Project Manager, Integrators) during the implementation phase, provide training and mentorship to develop teams and clients, ensuring successful adoption and utilization of GridOS Data Fabric.

• Own the design deliverables (Statement of Work, Solution Architecture documents), ensure traceability of requirements with solutions proposed. Accountable for the communication and enforcement of the Solution Design to Customer and Key Project Stakeholders.

• Continuous Improvement: Drive excellence in execution through continuous improvement in their segment or domain (best practices and reference material - reference architectures, reference solutions, stay current with the latest advancements in the technological domain)

• Technical Expertise: Build technical expertise in the field of their segment or domain. Deliver technical consultancy services on demand

Skills & Qualifications:

Must Have

• Significant experience as a Data Architect, Data Analyst, or BI Solution Architect.

• Strong understanding of data architecture patterns, especially data fabric, data mesh, or enterprise data hubs.

• Experience of data modeling and database design

• Experience of technologies like columnar, NoSQL databases, unstructured data, streaming analytics and event driven architectures

• Experience of traditional RDBMS based technologies e.g. SQL, ETL, BI and data migration

• Familiarity with predictive analytics, data visualization

• Knowledge of energy utility business processes: network operations, metering, asset lifecycle, and compliance.

• Experience working with TSOs/DSOs or utilities vendors

• Familiarity with CIM / CGMES standards, IEC 61970/61968, or other energy modeling frameworks.

• Ability to facilitate workshops and write clear functional specifications. Experience in a client-facing or consulting role, delivering technical solutions to enterprise clients.

• Knowledge with cloud-based data platforms and solutions (e.g. Azure, AWS, Google Cloud).

• Excellent interpersonal, oral/presentation and written communication skills in both technical and non-technical language.

• Mobile Software Architecture: Knowledge of mobile software architecture for field crew operations, offline support, and near-real time operation.

• Professional English level.

Nice-to-Have

• Experience with data platforms such as Talend, Informatica, Azure Synapse, Snowflake, or Denodo.

• Experience working in a containerized microservice based environment

• Working knowledge of data catalogs e.g. DataHub and definition of data quality

• Experience of OT/IT integration and challenges

• Familiarity with zero trust architectures

• Experience of Machine Learning Pipelines

• Understanding of data governance framew#orks (e.g., DAMA-DMBOK).

• Exposure to semantic layers (e.g., dbt, LookML, AtScale, or similar tools).

Soft Skills

• Strong communication and stakeholder management skills.

• Business-oriented mindset with technical literacy.

• Proactive, structured, and able to drive consensus across teams.

• Curious and motivated to modernize utility data ecosystems.

• Strong problem-solving abilities and capable of articulating specific technical topics or assignments

Additional Information

Relocation Assistance Provided: No

Additional Information