Data Architect

Description

• Write project documentation in accordance with deliverables, including detailed design specifications.
• Continuous Improvement: Drive excellence in execution through continuous improvement (best practices and reference material - reference architectures, reference solutions, stay current with the latest advancements in the technological domain)
• Contribute to technical analysis activities, such as requirements gathering and gap analysis of user requirements
• Interact with Product Development Teams, Commercial Teams, Customers, Solution Providers (Partner / Integrators), and other cross-functional teams as required for the solution and implement processes to ensure best use of GEV Vernova products and services.
• Effectively apply GEV Vernova execution methodology and project standards
• Effectively leverage product capabilities, driving standardization, limiting customization, and maximizing reuse of content developed for previous solutions.
• Contribute to change management and adoption across internal stakeholders and external partners.
• Drive workshops with customers, provide expert knowledge capturing the foundation for a successful GridOS Data Fabric realization.
• System optimization: Monitor system performance and troubleshoot data-related issues. Optimize data processes and queries for improved performance and scalability.
• Define domain-driven data models aligned with electric utility processes (grid operations, asset management, outage response, etc.).
• Data Analytics: Works on extracting meaningful patterns and trends from data to support decision-making and optimize operations
• Define data service contracts and semantic layers across operational and analytical use cases.
• Cross-functional collaboration: Work closely with data scientists, analysts, and product managers to understand data requirements and deliver effective solutions.
• Provide expert knowledge and experience to collaborate with the customer to identify technical requirements and estimate the technical effort required to implement complex software solutions.
• Act as a thought leader within the utility domain, understanding and challenging the utilites data and business processes.
• Support data cataloging, lineage tracing, and business glossary implementation.
• Ensure that data is discoverable, trustworthy, and accessible for use by analytics and AI teams.
• 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.
• Leadership: Serves as a mentor to new team members, coaching them in troubleshooting and problem-solving.
• Collaborate with solution architects to align logical data models with physical implementations & prepare a high level integration architecture design that integrates data from various sources with the GridOS Data Fabric, ensuring data quality, consistency, and accessibility.
• Serve as a technical leader or mentor on complex, integrated customer implementations, either within individual project teams and/or cross-organizationally
• Data quality and governance: Implement data validation and monitoring processes to ensure data accuracy, consistency, and reliability. Collaborate on data governance strategies, including data security and access controls.
• Collaborate with Project Managers, Delivery Managers, Solution Leads, DevOps and Services Consultants throughout the implementation of a project to identify and scope application changes while adhering to the change management process.
• Provide training and mentorship to develop teams and customers, ensuring successful adoption and utilization of GridOS Data Fabric.

Requirements

• Big data technologies: Experience with big data frameworks like Apache Spark or Kafka.
• Business Acumen: Experience delivering projects involving multiple stakeholders. Leverages knowledge of market and customer segment to establish further credibility in the eyes of the customer. Addresses customer concerns quickly and effectively; at times, anticipates customer concerns before they become issues.
• Cloud platforms: Familiarity with cloud platforms and their data services, such as AWS (e.g., S3, Glue, Redshift), Azure (e.g., Data Factory, Synapse), or Google Cloud Platform (e.g., BigQuery, Dataflow).
• Industry experience: 10+ years of experience in industrial systems related field. Demonstrated ability of implementing robust data governance frameworks while maintaining high standards for security and compliance.
• Communication: Strong verbal and written communication skills with the ability to explain technical concepts to non-technical stakeholders.
• Orchestration : Experience with workflow orchestration tools like Apache Airflow or Camel.
• Problem-solving: Excellent analytical and problem-solving abilities. Demonstrates ability to diagnose and solve advanced issues. Can isolate highly complex scenarios down to actionable items and drive solutions.
• Containers : Knowledge of containerization technologies like Docker and Kubernetes.
• SQL expertise: Strong knowledge of database systems and advanced SQL for data querying and optimization.
• Education: Bachelor's or master's degree in Computer Science, Engineering, Information Systems, or a related technical field.
• Programming: Advanced proficiency in programming languages commonly used in data engineering, such as Python or Java.
• Data modeling: Experience with data modeling techniques and best practices.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance