Associate Enterprise Architect – Data Analytics & AI
Location: Remote (Travel once or twice to meetings a year)
Duration: 2 + years
Position Summary
The Associate Enterprise Architect – Data Analytics & AI will support the Enterprise Architecture (EA) team in developing, documenting, and optimizing data and analytics architecture across the organization. This role is ideal for early-career professionals with foundational knowledge in data architecture, analytics platforms, and artificial intelligence (AI) concepts. The position plays a key role in helping the organization realize value from data through improved visibility, governance, and technology planning.
Key Responsibilities
Data & Analytics Architecture Support
•Assist in mapping business capabilities to data sources, analytics platforms, and data pipelines.
•Maintain and update architecture artifacts related to data lakes, warehouses, and analytics tools.
•Support initiatives around data governance, data lineage, and metadata management.
AI & Automation Enablement
•Contribute to the documentation of AI/ML model architecture, deployment strategies, and use cases.
•Help identify areas for AI application in business processes through collaboration with business and IT teams.
•Support architecture input for intelligent automation platforms, chatbots, and decision engines.
Repository & Tool Support
•Help maintain enterprise architecture models in tools such as LeanIX, ensuring accuracy and traceability of data and analytics components.
•Generate reports and visualizations to support technology planning and business engagement.
Collaboration & Governance
•Participate in architectural review sessions and working groups related to data analytics and AI.
•Work with solution architects, data scientists, and platform engineers to align data initiatives with architectural standards.
•Assist in developing and socializing reference architectures for data and AI.
Required Qualifications
•Bachelor's degree in Information Systems, Computer Science, Data Science, or related discipline.
•0–3 years of experience in IT, data analytics, or enterprise architecture support.
•Basic understanding of data platforms (e.g., SQL, data lakes, cloud storage).
•Familiarity with AI/ML fundamentals and common tools (e.g., Python, scikit-learn, Power BI).
•Strong documentation and analytical skills.
Preferred Qualifications
•Exposure to data architecture concepts such as ETL, data modeling, or metadata management.
•Academic or internship experience in enterprise analytics or AI initiatives.
•Knowledge of architecture frameworks such as TOGAF or Zachman.
•Interest in utilities, energy, or regulated industries.
Tools & Technologies
•Data Platforms: Snowflake, Data Lakehouse, SQL-based systems
•AI/ML Tools: Python, TensorFlow, MLFlow (preferred)
•Visualization Tools: Power BI, Tableau
•Architecture Tools: LeanIX (preferred), Lucidchart, Visio
•Cloud Platforms: Azure, AWS (basic understanding).
