Solution Architect - Data

Position Description

The Solutions Architect - Data & AI is master-level individual contributor and visionary technical leader who will drive the strategic design, architecture, and execution of enterprise-scale Data and Artificial Intelligence (AI) programs. This role is responsible for bridging high-level innovation strategy with robust, production-grade technical execution enabling the organization to harness generative AI, advanced analytics, and predictive modeling for grid reliability, forecasting, and process automation. Additionally, this position serves as the institutional champion and authority for the Agile Practice & Standards across the innovation pipeline. The Architect will define, mature, and enforce the Agile frameworks, DevSecOps integration patterns, and delivery standards that ensure the practice operates with maximum velocity, quality, and governance compliance.

Essential Job Tasks:

• Define target-state AI and data architecture, including feature stores, model registries, and inference patterns (batch/streaming/real-time).
• Establish MLOps standards (CI/CD for models, observability, drift monitoring, rollback) and integrate with enterprise platforms.
• Create reference implementations and reusable components for common forecasting, classification, optimization, and NLP use cases.
• Lead discovery and solution architecture for priority use cases in partnership with business owners.
• Oversee technical design reviews; ensure non-functional requirements (reliability, performance, security, cost) are met.
• Guide build-vs-buy decisions and vendor integration for AI services/tools.
• Serve as Agile Lead/Scrum Master for cross-functional squads; manage ceremonies, impediments, and continuous improvement.
• Drive product roadmaps, backlog health, and delivery metrics (velocity, cycle time, release predictability).
• Mentor product owners and engineers on Agile/DevOps best practices.
• Define data contracts, lineage, and quality SLAs for AI workloads; champion ethical AI and model risk management.
• Implement A/B testing, shadow deployments, and post-deployment monitoring to validate business impact.
• Develop dashboards and KPIs to track delivery health, including cycle time, throughput, quality, and value realization, and provide regular reporting to support data-driven decision-making.
• Work with Security/Compliance to implement role-based access, encryption, PII handling, and auditability across AI solutions.
• Maintain documentation and controls aligned to internal policies and regulatory requirements.
• Translate complex AI concepts into clear business value narratives; prepare executive updates and decision briefs.
• Facilitate workshops and training to grow AI literacy and Agile maturity across the enterprise.
• Architect and oversee the end-to-end implementation of advanced data platforms, Machine Learning (ML) models, and Generative AI applications (including custom AI agents, Retrieval-Augmented Generation [RAG] systems, and NLP tools).
• Design highly scalable, secure, and modern cloud-native data pipelines (ETL/ELT) and data lake structures that integrate operational utility data (e.g., smart meter data, grid load, financial forecasting, and safety logs).
• Establish and enforce standards for data classification, mandatory labeling, model safety, and bias prevention in alignment with our Enterprise AI & Data Policy.
• Partner with data science teams to architect predictive models for financial forecasting, load forecasting, and grid optimization.
• Perform deep technical reviews of third-party platforms (e.g., safety management tools, SaaS products) to evaluate security posture, SOC 2 compliance, and API integration pathways into the enterprise datalake.

Position Requirements:

• Bachelor's Degree in Mathematics, Statistics, Computer Science or related field + 6 years experience required.
• Experience with cloud platforms (Azure preferred), containerization, orchestration, and data engineering (SQL/ETL/streaming).
• Proficiency with ML frameworks (e.g., scikit-learn, TensorFlow/PyTorch) and MLOps toolchains (Azure ML, MLflow, Databricks, Kubernetes).
• Strong knowledge of Agile practices (Scrum/Kanban), product management, and DevOps.
• PMP Certification

Preferred Qualifications:

• Professional certification in Cloud Architecture (e.g., Google Professional Cloud Architect), Data Engineering or PMP certification.
• Advanced Agile/Scrum certifications (e.g., PMI-ACP, CSP, SAFe Program Consultant [SPC], or Certified Scrum Coach).
• Prior experience working in the public utility sector or an asset-heavy industrial environment.
• Experience designing and deploying generative AI agents and workflows under enterprise governance constraints.

Compensation Information:

$116,650 - $145,800 (min. - mid.)