Enterprise Data & AI Architect

Citizens Energy Group•Indianapolis, IN

3d•Hybrid

About The Position

The Enterprise Data & AI Architect is responsible for defining, governing, and evolving the organization’s data architecture to ensure data is reliable, secure, scalable, and usable across analytics, operational reporting, application integration, and emerging AI‑enabled solutions. This role develops end‑to‑end architecture patterns for data ingestion, lakehouse/warehouse design, modeling standards, and data access in cloud‑native environments, with a strong focus on AI‑ready data foundations and enterprise-grade generative AI enablement. The Enterprise Data & AI Architect leads and mentors a team of individual contributors, providing architectural direction, prioritization, and technical guidance while remaining accountable for architecture quality and consistency. This role partners closely with data engineering, analytics, application teams, and IT governance bodies to evaluate and adopt emerging technologies—including generative AI and advanced analytics—while ensuring responsible use, security, and compliance. The position provides architectural oversight through design reviews, technical standards, and strategic recommendations that align data and AI capabilities to business outcomes.

Requirements

Bachelor’s degree in information systems, Computer Science, Engineering or equivalent years of experience in data or software engineering roles
5+ years of progressively responsible experience in data engineering, analytics engineering, or data platform delivery, including demonstrated ownership of architecture/design decisions.
Strong experience in data modeling (conceptual/logical/physical), dimensional modeling, and designing analytics-ready datasets.
Demonstrated experience leading or mentoring individual contributors in data engineering, analytics engineering, or related technical disciplines.
Experience designing or supporting AI‑enabled data solutions, including data readiness for machine learning, advanced analytics, or generative AI use cases.
Proven ability to design secure, scalable data solutions including data integration patterns, access models, and governance controls (security/privacy by design).
Experience with source control (Git) and CI/CD practices using Azure DevOps
Familiarity with AI platform services and MLOps concepts, such as model lifecycle management, prompt/version control, monitoring, and integration with CI/CD pipelines
Strong communication and leadership skills, with the ability to engage technical and non-technical stakeholders across the organization.

Nice To Haves

Cloud data architecture experience with Microsoft Azure data services (e.g., lakehouse/warehouse patterns, orchestration, storage, and BI integration), including strong familiarity with Azure-native security patterns (RBAC, Key Vault, auditing).
Experience defining and operationalizing data governance practices (standards, stewardship handoffs, metadata/lineage expectations, quality controls).
Familiarity with modern lakehouse concepts and platform evolution (e.g., unification of data engineering/BI workflows) relevant to long-term architecture planning.
Experience supporting regulatory/compliance-aware environments (privacy, auditability, data retention, security requirements).
Relevant certifications such as Azure Solutions Architect and/or advanced data platform certifications.
Experience supporting AI‑assisted analytics and decision intelligence, including semantic models, feature reuse, and AI‑augmented BI or self‑service analytics patterns.
Understanding of responsible AI principles (fairness, transparency, security, privacy) and how they impact enterprise data architecture and governance design.

Responsibilities

Evaluate and guide adoption of emerging data and AI technologies, including generative AI, advanced analytics, and automation capabilities, ensuring alignment with enterprise architecture standards and governance requirements.
Design and maintain AI‑ready data architecture patterns, including data quality, lineage, metadata, security, and access controls required to support machine learning, AI‑assisted analytics, and embedded AI use cases.
Provide people leadership for assigned individual contributors, including work prioritization, technical mentorship, architecture guidance, and contribution to performance feedback and professional development.
Partner with IT governance and architecture review forums to assess new AI and data technologies, define architectural guardrails, and ensure responsible, secure, and compliant implementation.
Set and enforce governance, security, and compliance controls: RBAC/least privilege, encryption, auditing, data classification, and privacy-aware design; ensure designs support regulatory and industry expectations.
Provide architecture review and technical direction for data initiatives and application integrations (APIs, events, file-based interfaces), ensuring strong data contracts and consistent standards.
Evaluate and guide platform/tooling choices (including cloud-native services and emerging capabilities) and maintain a roadmap that improves reliability, scalability, and delivery speed.
Work within Agile/Scrum teams using modern DevOps and CI/CD practices for data development and deployment.
Perform other duties as assigned.