Enterprise Data Architect, Forward Deployed Engineering

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 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.

Role Overview

As Enterprise Data Architect within Forward Deployed Engineering, you are the architectural authority for how customer data becomes trustworthy, AI-ready fuel for agentic solutions on the Business AI Cloud. You will partner directly with FDE engineers and enterprise clients to design the data models, integration patterns, and governance standards that ground retrieval, multi-agent orchestration, and SLM/LLM workflows in accurate, compliant, well-structured enterprise data.

This is a hands-on, cross-engagement role: you will move across multiple concurrent customer deployments - spanning source systems such as AWS, GCP, SAP, Salesforce, AS/400, D365, and Outlook/M365 - codifying reusable data architecture patterns rather than solving each integration from scratch. You will also help scale the FDE organization's data maturity, mentoring engineers and building the playbooks that let Uniphore stand up new customer data foundations quickly, securely, and consistently.

Key Responsibilities

• Cross-Engagement Data Architecture: Serve as the principal data architecture authority across multiple concurrent FDE customer engagements, ensuring data models, pipelines, and integration designs are scalable, secure, and consistent with BAIC platform standards.
• AI-Ready Data Design: Define canonical and dimensional data models, embedding/vector store schemas, and retrieval-grounding data structures that power RAG pipelines, multi-agent orchestration, and SLM/LLM fine-tuning for customer solutions.
• Enterprise Integration Leadership: Architect integration patterns across enterprise source systems - AWS, GCP, SAP, Salesforce, AS/400, D365, Outlook/M365, and other ERP/CRM/legacy platforms - establishing connector strategies for both cloud and on-premise environments.
• Data Quality & Governance: Establish data quality, lineage, metadata, and governance standards so agentic AI systems operate on validated, auditable data; define observability and monitoring practices for data pipelines feeding production agents.
• Reusable Frameworks & Playbooks: Own and extend Uniphore's enterprise integration playbook and data architecture asset library, turning patterns learned on individual engagements into reusable frameworks that accelerate future deployments.
• Team Leadership & Mentoring: Mentor FDE engineers on data modeling, ETL/ELT design, and enterprise integration best practices; review and validate data architecture decisions across the FDE portfolio.
• Customer & Stakeholder Partnership: Act as a trusted data architecture advisor to senior customer stakeholders, translating ambiguous data landscapes and legacy system constraints into pragmatic, scalable designs.
• Build vs. Buy & Tooling Strategy: Evaluate data platform, ETL, and data quality tooling; lead POCs and make build-vs-buy recommendations balancing cost, performance, and time-to-value for customer deployments.
• Product & Platform Feedback: Partner with Product and Platform Engineering to feedback connector gaps, data architecture requirements, and customer pain points observed across engagements, shaping the BAIC data layer roadmap.
• Compliance & Security: Partner with Security and customer compliance teams to ensure data privacy, encryption, residency, and regulatory requirements are met across regulated industries (e.g., healthcare, financial services).

Qualifications

• 10+ years of experience in data architecture, data engineering, or enterprise data platform roles, including hands-on design of large-scale, cloud-based data systems.
• Deep expertise in data modeling - dimensional modeling, canonical/semantic data models, and ETL/ELT pipeline design - across relational and NoSQL systems.
• Hands-on proficiency in SQL and Python; experience with modern cloud data platforms (AWS, Azure, or GCP) and data warehouse/lakehouse technologies (e.g., Snowflake, Databricks).
• Proven experience integrating enterprise systems - ERP (SAP), CRM (Salesforce), legacy platforms (AS/400), and Microsoft ecosystems (D365, Outlook/M365) - across cloud and on-premise architectures.
• Experience designing data foundations for AI/ML or GenAI use cases, including retrieval-augmented generation, vector databases, and embedding pipelines, strongly preferred.
• Track record of establishing data governance, lineage, and quality standards across teams or client engagements.
• Prior experience in a client-facing, consulting, or forward-deployed engineering role, translating ambiguous business needs into technical data architecture.
• Strong communication skills, with the ability to bridge technical data architecture decisions and business stakeholder priorities.
• Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, or a related field.

Desired Skills

• Exposure to Agentic AI platforms, LLM/SLM fine-tuning, and multi-agent orchestration frameworks (LangChain, LangGraph, CrewAI).
• Experience with real-time streaming and messaging frameworks (e.g., Kafka) and CDC-based data ingestion.
• Familiarity with data security and compliance frameworks (e.g., HIPAA, PCI-DSS, GDPR, SOX).
• Experience with BI/analytics tooling (Tableau, Looker, or similar) for downstream reporting and self-service analytics.
• Contributions to industry thought leadership, playbooks, or whitepapers on enterprise data architecture or Agentic AI.