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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.

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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)

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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.

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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

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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