GenAI Architect and Analytics

Position: GenAI Architect and Analytics

Location: Minneapolis MN (3-4days work from client location)

Job type: Contract

14 years of working experience.

Role Summary

The Analytics & GenAI Architect is responsible for designing and governing enterprise-grade AI-enabled analytics solutions for reporting and advanced analytics. This role bridges traditional enterprise reporting/BI with GenAI-powered experiences (e.g. conversational BI insight assistance governed Q&A over KPIs).

The architect will define reference architectures design standards and delivery patterns to ensure use cases are scalable performant trusted and aligned to business outcomes. The role is expected to collaborate across client stakeholders and partner ecosystem teams including hyperscaler platforms where enterprise reporting and conversational BI scenarios are being demonstrated .

This is an architecture and governance leadership role-not a deep ML model training/research role.

Key Responsibilities

1) End-to-End Architecture for AI-enabled Analytics

Define solution architectures for Vector 1 use cases spanning data ingestion analytics modeling semantic layers BI consumption and GenAI interaction patterns

Design conversational analytics patterns that reliably answer business questions using governed KPIs and approved datasets

Establish architecture guardrails for accuracy latency security and scale across multiple use cases/pods

2) Semantic Layer KPIs and Enterprise Metrics Strategy

Lead the design of enterprise semantic models (metrics dimensions business definitions) that drive consistent results across dashboards and GenAI responses

Define single version of truth principles across reporting assets and GenAI experiences

Partner with BI teams to ensure semantic definitions are reusable and auditable

3) GenAI Design Patterns for Structured Analytics (Non-ML Heavy)

Define and standardize analytics-focused GenAI patterns such as:

o Prompt/context grounding for KPI and reporting queries

o Tool/function calling patterns to retrieve verified data (e.g. BI tools SQL APIs)

o Response validation patterns to reduce hallucinations and ensure explainability

Guide GenAI engineers in designing reliable conversational BI experiences and explain my report workflows aligned with enterprise analytics

4) Data Readiness Governance & Responsible AI

Partner with governance teams to ensure data is AI-ready (quality metadata lineage access control)

Define Responsible AI guardrails and operational standards (traceability transparency auditability policy-aligned access)

Ensure GenAI insights remain consistent with enterprise reporting outputs and data policies

5) Cross-Functional Leadership & Delivery Enablement

Act as the technical authority for Vector 1 delivery pods and guide design decisions across teams

Build reference assets: architecture blueprints design checklists reusable components and standards

Support use-case shaping with program and business leads ensuring feasibility and high-value sequencing (consistent with Vector 1 periodization approach)

Technology Landscape (Preferred Experience)

We are looking for architects who can apply strong patterns across platforms. Experience with one or more in each category is preferred.

Data & Analytics Platforms (Warehouse/Lakehouse)

Cloud data platforms and lakehouse/warehouse concepts (e.g. BigQuery Snowflake Databricks Synapse/Fabric Redshift etc.)

Data transformation and orchestration ecosystems (e.g. dbt concepts scheduling/orchestration patterns)

BI Reporting & Semantic Modeling

Enterprise BI ecosystems and semantic modeling (e.g. Looker/LookML Power BI semantic models Tableau semantic patterns ThoughtSpot etc.)

KPI definition metrics layer approaches governed reporting architectures

GenAI Platforms for Enterprise Analytics

Enterprise LLM platforms used for analytics experiences (e.g. Gemini Enterprise and/or equivalents) applied to:

o Conversational BI

o Reporting Q&A

o Insight explanation / narrative generation grounded in structured data

Familiarity with LLM integration patterns (RAG for enterprise knowledge structured retrieval tool-use patterns)

Governance Security & Observability

Data governance fundamentals: cataloging/metadata lineage privacy access control quality monitoring

AI/LLM observability concepts: evaluation safety/guardrails logging monitoring response quality

Note: Specific product expertise is less important than the ability to design scalable vendor-neutral architectures and translate them into actionable delivery standards.

Required Qualifications

10 years of experience in enterprise analytics / data / BI architecture roles

Strong background in analytics data modeling KPI frameworks and semantic layer design

Proven ability to design or govern large-scale reporting and analytics platforms

Hands-on understanding of applying GenAI to analytics workflows (conversational BI / report Q&A / insight assistance)

Strong stakeholder management experience: ability to translate business questions into scalable technical designs

Preferred Qualifications

Experience in regulated or complex analytics environments (healthcare/insurance strongly preferred)

Experience in multi-partner delivery models (client SI hyperscaler)

Prior experience establishing reference architectures and reusable delivery patterns across multiple teams/pods