Prin AI & Analytics Data Architect

Entrust•Shakopee, MN

8d•$160,797 - $235,836•Hybrid

About The Position

Entrust is seeking a Principal AI & Analytics Data Architect to join our Corporate IT organization to design and manage data systems, ensuring data quality, and providing technical leadership in data architecture. The position reports to the Chief Information Officer and is based in Shakopee, Minnesota, United States.

Requirements

12–15+ years in data architecture, analytics leadership, or platform design
Proven experience in designing and scaling: Enterprise analytics platforms, AI‑ready data foundations
Implement and maintain a Data Catalog platform
Strong command of: Cloud data ecosystems (AWS preferred), Analytics architectures (BI, semantic layers, metrics), AI‑specific data patterns (RAG, feature stores, vector data)
Deep understanding of governance, privacy, and security‑by‑design
Experience operating in complex, regulated, or security‑sensitive environments
Strong analytical and problem-solving skills.
Excellent communication and interpersonal abilities.
Ability to work independently and collaboratively in a team environment.

Nice To Haves

Prior ownership of an Analytics or Data function
Experience modernizing legacy analytics stacks
Expertise bridging: IT architecture, Analytics delivery, Business decision‑making
Background working directly with executive leadership
Familiarity with cloud platforms (e.g., AWS, Azure)
Knowledge of data security and privacy legislation.

Responsibilities

Implement and maintain a Data Catalog.
Standardize metadata tagging and data lineage to ensure AI teams clearly understand data sources.
Define a Semantic Layer (or metrics store) so key metrics are calculated consistently across AI models and dashboards.
Ensure data is optimized for AI consumption, including vectorization readiness, handling unstructured content (e.g., PDFs, images), and developing “gold” standard datasets.
Define and own the enterprise AI + analytics reference architecture
Architect data platforms that support: Executive analytics and BI, Machine learning and generative AI, AI agents and copilots
Establish standards across: Data lakes, warehouses, and lakehouse patterns, Semantic layers and governed metrics, Feature stores and vector data stores
Ensure the architecture scales for global, multi‑business use cases
Architect governance‑first data and AI patterns, not bolt‑ons
Ensure analytics and AI workloads comply with: Data classification and retention policies, Privacy, residency, and regulatory requirements
Integrate and operationalize: Data cataloging and lineage, DSPM and sensitive data monitoring
Shift the organization from: “Data pipelines” → Data products and governed assets
Define reusable patterns for: Ingest (ERP, CRM, SaaS, logs), Transform (ELT/semantic modeling), Serve (BI, APIs, AI, Copilots)
Enable safe, governed access to data for: Analysts, Data scientists, AI agents and copilots
Act as the executive‑facing authority on data, analytics, and AI
Present architecture, risks, and tradeoffs to: CIO, Executive Leadership Team
Set and enforce data and analytics standards across IT and product teams
Mentor senior architects, analytics leaders, and engineers