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.
Data Discovery & Inventory Leadership
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.
Search, Indexing & Retrieval
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.
Metadata & Knowledge Engineering
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.
Trust, Governance & Data Quality
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.
AI & Intelligent Discovery
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.
Who You’ll Work With
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.
What Makes You a Qualified Candidate
Required 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).
What You’ll Bring
Data Catalog & Metadata Platforms
Hands-on experience with technologies such as
Microsoft Purview, Fabric, Fabric IQ
Collibra
Alation
DataHub
Apache Atlas
Informatica Enterprise Data Catalog
Atlan
OpenMetadata
Search & Discovery Technologies
Deep expertise in
Elasticsearch / OpenSearch
Solr
Lucene
Vector databases
Semantic search architectures
Enterprise search platforms
Relevance ranking systems
Data Platforms
Experience across
Databricks
Snowflake
Microsoft Fabric
Azure Data Lake
Delta Lake
Hadoop ecosystems
Kafka and streaming platforms
Modern data warehouses
Metadata & Knowledge Technologies
Experience with
Knowledge graphs
Graph databases
Semantic technologies
Ontologies and taxonomy design
Entity resolution
Data lineage frameworks
Master data management
AI & Machine Learning
Experience applying
Large language models
RAG architectures
Agentic workflows
Metadata enrichment using AI
Semantic retrieval systems
AI-powered recommendations
What Distinguished Candidates Demonstrate
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.
Success Metrics
Within the first 12-18 months, this leader will
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.
Why We Think You'll 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
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