Our Company

Teradata is the connected multi-cloud data platform for enterprise analytics company. Our enterprise analytics solve business challenges from start to scale. Only Teradata gives you the flexibility to handle the massive and mixed data workloads of the future, today.

The Teradata Vantage architecture is cloud native, delivered as-a-service, and built on an open ecosystem. These design features make Vantage the ideal platform to optimize price performance in a multi-cloud environment.

Senior Solution Analyst - AI Data Products

We are seeking a skilled Senior Solution Analyst to support the development and delivery of enterprise generative AI and agentic solutions. This role combines hands-on data analysis, data modeling, and solution design with practical prompt engineering skills. You will play a critical role in building the AI-ready data foundation—ensuring data is properly mapped, modeled, and architected to enable intelligent agents to navigate and generate actionable insights across the enterprise.

What You'll Do

Data Analysis & Mapping:

• Perform comprehensive data discovery and profiling to understand source systems, data quality, and business context
• Create and maintain detailed source-to-target data mappings that document data lineage, transformations, and business rules
• Analyze data relationships, dependencies, and anomalies to ensure accurate representation in downstream AI systems
• Collaborate with business stakeholders to validate data definitions, business logic, and domain-specific requirements
• Document data dictionaries, glossaries, and metadata to support enterprise knowledge management

Data Modeling & Layered Architecture:

• Design and implement logical and physical data models following enterprise modeling standards and best practices
• Adhere to layered data architecture standards (Bronze/Silver/Gold, Raw/Curated/Consumption) ensuring data flows correctly through transformation stages
• Build semantic data models that represent business entities, relationships, and hierarchies optimized for AI agent consumption
• Define and maintain dimension and fact tables, slowly changing dimensions (SCDs), and aggregate structures
• Ensure data models support both analytical workloads and AI/ML feature engineering requirements
• Collaborate with data architects to align models with enterprise data architecture patterns and governance standards

AI Foundation & Knowledge Base Development:

• Build and curate knowledge bases that provide contextual grounding for AI agents and LLM-powered applications
• Structure data assets with rich metadata, annotations, and semantic tags to enhance AI agent understanding
• Design data structures optimized for RAG (Retrieval-Augmented Generation) pipelines and vector embeddings
• Ensure data quality, consistency, and completeness standards are met for AI-ready datasets
• Create and maintain entity relationships, ontologies, and taxonomies that enable intelligent agent navigation

AI Solution Development & Support:

• Support product strategy and roadmap execution for AI-powered data products within scrum teams
• Implement and test prompt engineering strategies for multi-agent systems and enterprise agentic use cases
• Contribute to prompt libraries, templates, and frameworks ensuring consistency across AI applications
• Validate AI agent outputs against source data to ensure accuracy, completeness, and business relevance
• Document agent workflows, data access patterns, and reasoning chains for enterprise deployments

AI Tool Evangelism & Enablement:

• Champion adoption of AI tools and technologies across business teams, demonstrating practical use cases and benefits
• Conduct training sessions, workshops, and demos to enable end-users and stakeholders on AI capabilities
• Create user guides, quick-start materials, and best practice documentation for AI tool adoption
• Gather user feedback and advocate for improvements to enhance AI tool usability and adoption rates
• Build and nurture an internal community of AI tool users, sharing tips, success stories, and lessons learned

Business Analysis & Reporting:

• Translate business requirements into technical data specifications and AI solution designs
• Create dashboards, reports, and visualizations to track data quality, AI performance, and business KPIs
• Support ROI analysis and business value demonstration through data collection and metrics tracking
• Prepare documentation and presentations on solution capabilities for stakeholders

Impact & Success

Contribute to organizational AI capabilities by building the robust data foundation that enables intelligent agents to deliver measurable business value. Success includes delivering well-documented data models and mappings, achieving high data quality scores for AI-ready datasets, driving AI tool adoption across teams, and contributing to production-ready agentic AI system deployments.

Team & Reporting

Collaborate with cross-functional teams including data architects, data engineers, ML engineers, solution architects, and business stakeholders. Work closely with AI Product Owners and Knowledge Base Architects to ensure data models and mappings align with enterprise AI strategy. Reports to Senior Manager or Director within the Analytics CoE.

Required Qualifications

Experience:

• 5-8 years in data analytics, data modeling, or business systems analysis in enterprise environments
• 3+ years hands-on experience with data mapping, ETL/ELT processes, and data transformation logic
• 3+ years designing dimensional models, star/snowflake schemas, or semantic data models
• 2+ years experience with prompt engineering and LLM platforms (ChatGPT, Claude, Gemini)
• 1+ years exposure to agentic AI concepts, RAG architectures, or knowledge base development
• Strong proficiency in SQL; experience with Python or other scripting languages preferred

Core Skills:

• Data Modeling: Expertise in logical/physical modeling, dimensional design, and semantic layer development
• Data Mapping & Lineage: Ability to create comprehensive source-to-target mappings with transformation rules
• Layered Architecture: Understanding of medallion architecture (Bronze/Silver/Gold) and data lakehouse patterns
• AI/ML Foundation: Knowledge of data preparation for AI, feature engineering, and knowledge base structures
• Prompt Engineering: Ability to design and test effective prompts producing quality, business-relevant outputs
• Data Visualization: Proficiency with BI tools (Power BI, Tableau) for dashboards and reporting

Communication & Collaboration:

• Strong presentation skills for training sessions, demos, and stakeholder updates
• Ability to explain technical data concepts and AI capabilities to non-technical audiences
• Enthusiasm for evangelizing AI tools and driving adoption across diverse teams
• Excellent documentation skills for data dictionaries, mappings, and technical specifications
• Collaborative mindset with ability to work effectively across data engineering, architecture, and business teams

Technical Knowledge:

• Understanding of enterprise data warehousing concepts, data lakes, and modern data platforms
• Familiarity with cloud data platforms (Azure, AWS, GCP) and their data services
• Knowledge of RAG architectures, vector databases, and embedding concepts for AI applications
• Awareness of data governance principles, data quality frameworks, and responsible AI practices
• Familiarity with metadata management tools and data cataloging solutions

Preferred Qualifications

• Experience with data modeling tools (ERwin, PowerDesigner, dbt)
• AI/ML certifications or cloud platform certifications (Azure, AWS, GCP)
• Experience with LangChain, vector databases (Pinecone, Weaviate), or knowledge graph technologies
• Background in training delivery, change management, or technology adoption programs
• Experience with agile methodologies and tools (Jira, Rally)
• Knowledge of specific domain data (Finance, Sales, Marketing, HR) and industry data standards

Personal Attributes

Curious and detail-oriented professional with passion for data quality and AI technologies. Strong communicator who enjoys teaching others and driving technology adoption. Analytical thinker who can bridge the gap between raw data and AI-ready information products. Collaborative team player who thrives in fast-paced, evolving environments where data and AI intersect.

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