TPM-AI Product Owner
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 foundationâensuring 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.
#LI-NT1
Why We Think Youâll Love Teradata
We prioritize a people-first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well-being because we care about our people and their ability to thrive both personally and professionally. We are committed to actively working to foster an inclusive environment that celebrates people for all of who they are.
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 foundationâensuring 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.
#LI-NT1
Why We Think Youâll Love Teradata
We prioritize a people-first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well-being because we care about our people and their ability to thrive both personally and professionally. We are committed to actively working to foster an inclusive environment that celebrates people for all of who they are.