Principal Data Architect, Forward Deployed Engineering

Uniphore is the Business AI company. Our sovereign, composable and secure AI platform connects enterprise data, fine-tunes AI models and deploys agentic AI across the enterprise. We empower every worker to boost productivity and help businesses grow faster, operate smarter and reduce costs. Trusted by more than 2,000 businesses globally, and recognized on the Deloitte Fast 500, Uniphore delivers on the promise of AI as a transformative force for business. Job Description:Uniphore is The Business AI Company. We enable businesses to rapidly adopt, significantly transform, and immediately unlock value through AI. Inspired by the simplicity of consumer AI, and with a deep understanding of the scalability and security required for business, we provide a platform that allows business users to effortlessly harness agentic AI, tapping into enterprise knowledge grounded in their own proprietary data. Through our core principles of composable, sovereign, and secure AI, we are committed to unlocking AI’s potential as a transformative force for businesses — with openness, trust, and scalability unmatched by any other solution. Our Business AI Cloud (BAIC) is a complete AI platform to power the Agentic Enterprise: it unifies data, knowledge, models, and agents in a secure, composable stack, enabling business users to deploy AI agents instantly and IT leaders to scale trusted, enterprise-grade applications. As Principal Data Architect within Forward Deployed Engineering, you are the senior-most data authority on our most complex customer engagements. You lead the data and knowledge foundations that power customer AI solutions on the Business AI Cloud, working directly with enterprise architects, data leaders, and FDE engineers. What distinguishes this role is how the work gets done. Rather than hand-building every pipeline, you lead data discovery and transformation journeys through BAIC’s Data Agents — Data Discovery, Data Engineering, Data Analyst, and Data Orchestrator — compressing work that traditionally consumes months of an enterprise data program into a fraction of the time. Your judgment directs the agents: deciding what to profile, how to model, where to intervene, and when the output is trustworthy enough to build on. On top of that accelerated foundation, you build what makes the platform valuable: knowledge semantic clusters, ontologies, and the semantic structures that ground BAIC agents in each customer’s proprietary business reality. Knowledge is the heart of this role. We are not a data migration practice — we meet customers where their data already lives and make it usable, governed, and intelligible to AI. The role is hands-on and customer-facing across multiple concurrent deployments, but the scope is broader than any single engagement: you set the standards the wider FDE organization delivers against, act as the escalation point for the hardest data problems, mentor other architects and engineers, and turn field learnings into reusable patterns. Key Responsibilities Agent-Led Data Discovery and Transformation. Lead customer data discovery, profiling, and transformation journeys using BAIC’s Data Agents — Data Discovery, Data Engineering, Data Analyst, and Data Orchestrator. Direct and supervise agent-driven work, review and correct generated models and pipelines, and establish the quality bar and human checkpoints that make accelerated delivery trustworthy at enterprise scale. Knowledge Semantic Clusters, Ontologies, and Graphs. Design the knowledge layer that sits on top of discovered and transformed data: semantic clusters, enterprise ontologies, taxonomies, and knowledge graphs that unify entities, relationships, and business meaning across many source systems and knowledge bases. Ground BAIC agents in customer-specific business context using RDF/OWL and SPARQL, property-graph, or hybrid graph-and-vector approaches as the engagement requires. Enterprise Data Architecture and Platform Advisory. Advise customers on target-state data architecture spanning conceptual, logical, and physical models, and on platform choices across Snowflake, Databricks, BigQuery, Redshift, and Synapse with open table formats such as Delta Lake and Apache Iceberg. Apply federated patterns such as data mesh and data fabric where they fit the customer’s maturity, and design to the customer’s existing estate rather than replacing it. Enterprise Integration, Master Data, and Change Data Capture. Define ingestion patterns across systems such as SAP, Salesforce, AS/400, D365, and Microsoft 365, and across AWS, Azure, and GCP. Establish master data management and entity resolution across fragmented enterprise systems. Design real-time and near-real-time CDC ingestion with Debezium, Fivetran, Kafka, or native log-based capture. AI-Ready Data Foundations and Feature Stores. Design document processing, metadata enrichment, chunking, embedding, and vector indexing strategies that enable accurate retrieval and grounding for LLM/SLM applications and agentic workflows. Design feature stores — Feast, Tecton, Databricks Feature Store, or SageMaker — with online/offline parity and point-in-time correctness. Conversational and Agentic Data Access. Design the semantic and metric layers that make natural-language-to-SQL reliable at enterprise scale, including schema grounding, business glossary alignment, query generation guardrails, and accuracy evaluation harnesses, so business users and BAIC agents can query governed enterprise data in natural language with trustworthy results. Governance, Security, and the AI Data Catalog. Establish validation, lineage, ownership, and lifecycle standards with measurable quality checks and monitoring for data feeding production AI — including for agent-generated artifacts. Implement AI-ready cataloging and active metadata through Unity Catalog, Microsoft Purview, Collibra, Alation, DataHub, or Atlan. Partner with Security and customer teams on access controls, sensitive-data handling, retention, encryption, residency, and permission-aware retrieval. Technical Leadership and Mentorship. Build prototypes, validate approaches, review implementations, and troubleshoot quality, performance, and scalability issues alongside FDE engineers. Develop reference architectures and reusable assets that raise delivery consistency across engagements. Lead discovery workshops, support technical deal shaping with Sales and Solutions, and mentor engineers and other architects. Product and Platform Partnership. Serve as a primary field voice into Product and Platform Engineering. You are closest to how the Data and Knowledge Layer and the Data Agents actually behave against messy, real-world enterprise data, and that perspective is expected to shape the roadmap rather than simply report on it. Work in close partnership with Product Managers and platform engineers to surface critical gaps early, distinguish one-off customer asks from systemic patterns worth building for, and bring forward-looking signal on where enterprise data and agentic AI are heading. Propose and pressure-test platform capabilities, contribute to design reviews, and advocate for the connector, ontology, catalog, and agent improvements that will matter to the next ten customers, not just the current one. Required Qualifications 12+ years in data architecture, data engineering, or enterprise data platforms, including hands-on design and delivery of large-scale data systems. Sustained experience in complex enterprise data environments — multi-year programs spanning many source systems, business domains, and stakeholder groups — not solely short-cycle or single-system projects. Experience as the senior-most data architect on multiple concurrent enterprise engagements, setting standards that other engineers deliver against. Demonstrated experience designing ontologies, knowledge graphs, semantic clusters, or enterprise semantic layers spanning multiple systems. This is the core of the role. Deep expertise in relational and NoSQL data modeling, dimensional and semantic models, and ETL/ELT pipeline design. Working knowledge of modern warehouse and lakehouse platforms — Snowflake, Databricks, BigQuery, Redshift, or Synapse — and open table formats, sufficient to architect and advise on customer data platforms. Strong hands-on proficiency in SQL and Python, and comfort with distributed processing such as Spark. Practical experience with master data management and entity resolution across fragmented enterprise systems. Demonstrated experience integrating ERP, CRM, collaboration, or legacy systems across cloud and on-premises environments, including CDC and streaming ingestion. Experience establishing data quality, metadata, lineage, and governance practices for production systems. Comfort working alongside AI agents and copilots as delivery tools — directing, reviewing, and correcting generated output rather than treating automation as a black box. Experience in a customer-facing engineering, consulting, or technical delivery role, with the ability to translate business requirements into practical designs and explain tradeoffs to both technical and executive audiences. Track record of influencing product direction from the field — translating what was learned on real deployments into concrete, prioritized input that engineering teams acted on. Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, or a related field, or equivalent practical experience. Preferred Qualifications Experience building data foundations for GenAI applications, including RAG, embedding pipelines, vector databases, and retrieval evaluation. Experience delivering natural-language-to-SQL or text-to-SQL at enterprise scale, including accuracy evaluation and guardrail design. Production experience with graph databases such as Neo4j, Amazon Neptune, or TigerGraph, or with RDF and SPARQL at scale. Experience implementing data mesh or data fabric architectures within a large, federated enterprise. Exposure to agentic AI frameworks such as LangChain, LangGraph, CrewAI, or the Model Context Protocol. Experience supporting data security, residency, and regulatory requirements in healthcare, financial services, public sector, or other regulated industries. Track record of creating reusable data architecture frameworks, technical playbooks, or reference implementations adopted by other teams. Preferred Qualifications Experience building data foundations for GenAI applications, including RAG, embedding pipelines, vector databases, and retrieval evaluation. Experience delivering natural-language-to-SQL or text-to-SQL at enterprise scale, including accuracy evaluation and guardrail design. Production experience with graph databases such as Neo4j, Amazon Neptune, or TigerGraph, or with RDF and SPARQL at scale. Experience implementing data mesh or data fabric architectures within a large, federated enterprise. Exposure to agentic AI frameworks such as LangChain, LangGraph, CrewAI, or the Model Context Protocol. Experience supporting data security, residency, and regulatory requirements in healthcare, financial services, public sector, or other regulated industries. Track record of creating reusable data architecture frameworks, technical playbooks, or reference implementations adopted by other teams. Hiring Pay Range:$232,900 - $291,100Benefits: In addition to competitive base pay, this position also includes an annual incentive opportunity based on target achievement, pre-IPO stock options, benefits including medical, dental, vision, 401(k) with a match, and more, plus generous paid time off, paid holidays, paid day off for your birthday and other paid leave policies to support employees through all phases of life.Location preference:USA - CA - Palo AltoUniphore is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.For more information on how Uniphore uses AI to unify—and humanize—every enterprise experience, please visit  USA - CA - Palo AltoType: Full time