Principal Engineer, Domain Knowledge
Teradata
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Job summary
Teradata is seeking a Principal Engineer, Domain Knowledge, to define and build the next generation of enterprise-scale data discovery, inventory, and metadata management capabilities across Finance, Healthcare, and other verticals.
You will own architecture, engineering, and evolution of systems that enable analysts, data scientists, AI agents, and business users to discover, understand, trust, and govern data assets across the organization.
Qualifications
- 12+ years of experience building large-scale data platforms, metadata systems, search platforms, or knowledge management technologies.
- 5+ years leading architecture and implementation of enterprise data catalog, metadata management, or data discovery platforms.
- Proven track record designing systems that inventory and index thousands to millions of enterprise data assets.
- Deep experience with metadata ingestion, metadata modeling, lineage, governance, and data asset management.
- Strong software engineering background with demonstrated expertise in distributed systems and platform engineering.
- Experience building highly available, cloud-scale systems running in production environments.
- Expertise in at least one major cloud platform (Azure, AWS, or GCP).
Responsibilities
- Lead development of metadata models, business glossaries, taxonomies, ontologies, and enterprise information architectures.
- Establish standards for technical, business, operational, and governance metadata.
- Design lineage capture and metadata synchronization frameworks across heterogeneous ecosystems.
- Build metadata knowledge graphs connecting datasets, pipelines, dashboards, reports, AI models, metrics, owners, and business domains.
Skills
Education
Tools
Job description
At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.
At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.
We are seeking a Principal Engineer , Domian Knowledge, to define and build the next generation of enterprise-scale data discovery, inventory, and metadata management capabilities across many verticals, such as Finance, Healthcare, etc. This is a highly strategic and hands-on technical leadership role for an industry-recognized expert who has successfully designed and deployed large-scale data catalog platforms across complex enterprises.
You will own the architecture, engineering, and evolution of systems that enable employees, analysts, data scientists, AI agents, and business users to discover, understand, trust, and govern data assets across the organization. Think of this role as building and operating the enterprise's "library index" or "parts catalog" for data, where every dataset, table, document, API, feature, metric, model, and knowledge asset can be found, understood, and evaluated for use.
You will own the strategy and technical execution of Teradata's Enterprise Context Engine Domain Knowledge — developing enterprise-leading capabilities to extract, curate, and continuously evolve structured metadata and business context supported by Teradata’s industry knowledge models to serve as a governed layer powering both human analyst acceleration and AI agent grounding. This role combines deep product thinking with ecosystem and platform leadership, spanning the full lifecycle from knowledge acquisition and curation to runtime delivery and continuous improvement.
The Context Engine integrates robust database and catalog connectivity, intelligent query parsing, and Teradata's decades of industry knowledge — particularly in highly regulated enterprises — into a curated, continuously improving context layer. Context Engine is a platform that converts enterprise data estates into governed, agent-ready knowledge. Built for organizations where AI reliability and auditability are non-negotiable, Context Engine enables companies to deploy AI workflows on the data they already own — without rebuilding their infrastructure, without retraining their models, and without waiting for generic AI synthesis to learn the business terminology that took their analysts years to master.
The ideal candidate combines deep technical expertise in metadata management, distributed systems, data platforms, search and indexing technologies, governance, lineage, and data quality with a passion for creating exceptional discovery experiences at enterprise scale.
- Design and build enterprise-wide data catalog and metadata platforms that continuously discover, inventory, classify, and organize data assets.
- Create systems that automatically scan and ingest metadata from databases, data lakes, warehouses, streaming platforms, APIs, BI tools, AI platforms, and SaaS applications.
- Drive the vision for making organizational data as discoverable as products in an e-commerce catalog or books in a modern library system.
- Establish a unified metadata strategy spanning structured, semi-structured, and unstructured data assets.
- Architect highly scalable metadata indexing and search systems supporting millions to billions of metadata entities.
- Design semantic search, keyword search, metadata search, faceted navigation, and relevance-ranking capabilities.
- Leverage graph technologies, vector search, embeddings, and knowledge representations to improve discoverability.
- Build intelligent recommendation systems that help users identify trusted and relevant data assets.
- Lead development of metadata models, business glossaries, taxonomies, ontologies, and enterprise information architectures.
- Establish standards for technical, business, operational, and governance metadata.
- Design lineage capture and metadata synchronization frameworks across heterogeneous ecosystems.
- Build metadata knowledge graphs connecting datasets, pipelines, dashboards, reports, AI models, metrics, owners, and business domains.
- Integrate data quality, stewardship, ownership, compliance, and governance indicators into catalog experiences.
- Create mechanisms for trust scoring, certification, usage analytics, freshness monitoring, and policy enforcement.
- Partner with governance, compliance, privacy, and security organizations to ensure catalog accuracy and regulatory alignment.
- Drive enterprise-wide adoption of metadata standards and governance practices.
- Enable AI-powered data discovery experiences using LLMs, semantic understanding, and agentic workflows.
- Design retrieval frameworks that allow AI agents to identify, evaluate, and consume trusted enterprise data assets.
- Create metadata enrichment systems leveraging machine learning and generative AI capabilities.
- Develop next-generation conversational discovery experiences for enterprise knowledge workers.
- Serve as a principal architect and thought leader across multiple engineering and data platform organizations.
- Influence enterprise data strategy and long-term platform investments.
- Mentor senior engineers and architects.
- Drive architecture reviews, technical standards, and engineering excellence across the metadata ecosystem.
- 12+ years of experience building large-scale data platforms, metadata systems, search platforms, or knowledge management technologies.
- 5+ years leading architecture and implementation of enterprise data catalog, metadata management, or data discovery platforms.
- Proven track record designing systems that inventory and index thousands to millions of enterprise data assets.
- Deep experience with metadata ingestion, metadata modeling, lineage, governance, and data asset management.
- Strong software engineering background with demonstrated expertise in distributed systems and platform engineering.
- Experience building highly available, cloud-scale systems running in production environments.
- Expertise in at least one major cloud platform (Azure, AWS, or GCP).
Hands-on experience with technologies such as:
- Microsoft Purview, Fabric, Fabric IQ
- Collibra
- Alation
- DataHub
- Apache Atlas
- Informatica Enterprise Data Catalog
- Atlan
- OpenMetadata
Deep expertise in:
- Elasticsearch / OpenSearch
- Solr
- Lucene
- Vector databases
- Semantic search architectures
- Enterprise search platforms
- Relevance ranking systems
Experience across:
- Databricks
- Snowflake
- Microsoft Fabric
- Azure Data Lake
- Delta Lake
- Hadoop ecosystems
- Kafka and streaming platforms
- Modern data warehouses
Experience with:
- Knowledge graphs
- Graph databases
- Semantic technologies
- Ontologies and taxonomy design
- Entity resolution
- Data lineage frameworksMaster data management
Experience applying:
- Large language models
- RAG architectures
- Agentic workflows
- Metadata enrichment using AI
- Semantic retrieval systems
- AI-powered recommendations
- Recognized leadership in the metadata, catalog, search, or knowledge management domains.
- Experience building enterprise-wide data discovery platforms adopted by tens of thousands of users.
- Proven experience delivering knowledge management systems in on-prem, regulated, sovereign and air-gapped environments
- Demonstrated ability to scale metadata systems across large, globally distributed organizations.
- Strong product thinking around discoverability, trust, usability, and developer experience.
- Ability to bridge data engineering, governance, AI, and business stakeholder communities.
- History of driving transformational initiatives from concept through broad enterprise adoption.
- Deliver a unified enterprise inventory of critical data assets.
- Improve discoverability and reuse of trusted datasets across business domains.
- Increase metadata completeness, ownership coverage, and lineage visibility.
- Enable AI-assisted data discovery and self-service analytics experiences.
- Reduce time-to-find and time-to-trust for enterprise data assets.
- Establish a scalable metadata foundation powering governance, analytics, and AI across the organization.
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
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 an anti-racist company because our dedication to Diversity, Equity, and Inclusion is more than a statement. It is a deep commitment to doing the work to foster an equitable environment that celebrates people for all of who we are.
Teradata invites all identities and backgrounds in the workplace. We work with deliberation and intent to ensure we are cultivating collaboration and inclusivity across our global organization. We are proud to be an equal opportunity and affirmative action employer. We do not discriminate based upon race, color, ancestry, religion, creed, sex (including pregnancy, childbirth,
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