Technical Architect Jobs in Ohio

Role Overview

We are seeking a Technical Architect (TA) with deep expertise in Data, Analytics, and Artificial Intelligence (AI) to join the IT Enterprise Architecture organization. This role is accountable for proactively leading data‑, analytics‑, and AI‑driven technology transformation initiatives and enabling measurable business outcomes across the enterprise. The Technical Architect will play a critical role in transforming local, legacy, data‑driven processes, and systems into centralized, scalable, and group‑wide platforms, while ensuring alignment with enterprise architecture standards and business strategy. Technical Architects provide technical leadership across analysis, design, facilitation, and execution, supporting the evolution of enterprise Data, Analytics, and AI capabilities and the associated application portfolios and technology stacks. The role owns the creation of key architectural deliverables such as target‑state architectures, transformation roadmaps, standards, and guidelines to enable successful project delivery and long‑term strategic outcomes. This position is based in the USA and ensures that Data, Analytics, and AI architecture vision, principles, and standards are consistently executed through a common enterprise framework, with a strong emphasis on cloud‑based data platforms, AI enablement, and data governance. The ideal candidate will help advance organizational directives around simplification, modernization, and innovation by providing architectural leadership in enterprise data platforms, integration components, and AI‑enabled data strategies.

Key Responsibilities

- Assist in the development of a multi‑year Data, Analytics, and AI roadmap, aligned with the Munich Re Target Architecture and Roadmap Development Process, in collaboration with Data & Analytics Enterprise Architects.
- Drive standardization of Data, Analytics, and AI technology standards, principles, and guidelines across multiple business entities.
- Define and maintain technical standards for enterprise data management, analytics platforms, and AI enablement capabilities.
- Design and guide data‑centric and AI‑enabled initiatives, supporting the transition from traditional data architectures to next‑generation cloud, analytics, and AI platforms.
- Act as an evangelist and ambassador for enterprise architecture standards including Data Governance, Data Intake and Ingestion, Data Modeling, Data Integration, Analytics and AI lifecycle management.
- Collaborate closely with Business Solutions teams, Technology Architects, and Enterprise Data Architects across initiatives and implementations.
- Identify technology‑related business pain points by mapping business capabilities to current platforms, leveraging EA practices and participating in innovation activities, including AI adoption.
- Enable IT development and infrastructure teams to make informed technology decisions through frameworks, reference architectures, standards, and reusable patterns.
- Identify technical risks, architectural gaps, and vulnerabilities that could impact project delivery or lead to post‑release defects.
- Reduce cost and complexity through standardization, reuse, and rationalization of data, analytics, and AI platforms.
- Partner with EA and TA peers (enterprise, solution, and business architects) to derive the future‑state technology architecture, aligned to business strategy and external trends.
- Define migration and transformation plans to close gaps between current and target states, in alignment with Business Solutions and Business Technology Architects.
- Support governance, assurance, and compliance activities to ensure alignment with enterprise architecture standards and policies.
- Assess and articulate the organizational, skills, process, and financial impact of changes to the application portfolio, data platforms, and AI stack.
- Define and govern enterprise AI architecture standards, including model lifecycle management, MLOps, and AI platform integration.
- Ensure responsible and compliant AI adoption, aligned with AI governance, model risk management, data privacy, and security controls.
- Guide the integration of AI/ML capabilities into analytics platforms, including predictive, prescriptive, and generative AI use cases.
- Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions.
- Establish architectural patterns for AI model deployment, monitoring, versioning, and retraining in cloud environments.
- Evaluate emerging AI technologies, tools, and platforms and provide strategic recommendations for enterprise adoption.

Your Profile

- 4+ years of experience in Enterprise Architecture or Technical Architecture.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Mathematics, or Business (or equivalent).
- Strong experience with cloud platforms and services, including:
- Azure (e.g.; Azure AI Studio, Azure Data Services and tools)
- AWS (e.g.; Amazon Bedrock, Sagemaker, Data Services and tools)
- Databricks
- Hands‑on experience with enterprise data concepts, including:
- Data Intake and Ingestion
- Data Warehousing
- Data Lakes / Lakehouse architectures
- ETL / ELT
- Interactive and operational reporting
- Statistical and regulatory reporting
- Master Data Management (MDM)
- Data Governance, Quality, Security, Audit, Balance & Control
- Solid understanding of enterprise architecture practices, including:
- Architectural patterns
- Roadmaps
- Architecture Review Boards
- Solution Design Boards
- Experience defining data management and AI roadmaps, cloud‑based services, and reusable architectural patterns.
- Experience integrating operational data with enterprise data lakes.
- Strong understanding of data integration challenges and solution patterns.
- Experience with statistical and data science languages such as Python and R (strong asset).
- Exposure to AI/ML concepts, including model development, deployment, monitoring, and MLOps (required).
- Familiarity with Generative AI concepts, AI platforms, and enterprise adoption considerations (strong asset).
- Strong business acumen with deep understanding of:
- Financial systems
- Corporate and back‑office systems
- Enterprise data management, analytics, and AI technology landscape
- Strong problem‑solving skills, unquestioned integrity, and high collaboration capability.
- Passion for innovation, continuous improvement, modernization, and change management.
- Excellent written and verbal communication skills, with the ability to communicate effectively at all levels.
- High sense of ownership, accountability, and pride in delivered outcomes.