Job description

Qualifications: This is not a report developer or visualization designer role. We require platform infrastructure engineers, distributed systems specialists, and API-first architects to drive extreme scalability, harden data security, and lead the integration of Tableau with Enterprise Generative AI and autonomous agents. Core Deliverables • Tableau Studio: Enterprise-wide rollout, lifecycle governance, self-serve onboarding, and creator community adoption. • Tableau Data Apps: Architecture and deployment of interactive, web-based analytics applications using modern embedding frameworks (Embedding API v3, VizQL Data Service). • Endor + Tableau (EA): Deep integration with Enterprise Assistant (EA) and autonomous BI agents; providing certified semantic grounding to eliminate LLM hallucinations while preserving dynamic user entitlements. • MCP Server Enablement: Production Model Context Protocol (MCP) pipelines exposing Tableau schemas, metadata, and query engines to developer and analyst AI workflows. Required Technical Competencies • Cluster Scalability & Distributed Engines: • Multi-node cluster architecture and tuning under heavy peak concurrency. • Native integration and query performance optimization with modern engines: Trino, StarRocks, and Snowflake. • Workload optimization: Hyper extract caching, backgrounder scheduling, and query bottleneck analysis. • Security, Entitlements & Compliance: • Dynamic Row-Level (RLS) and Column-Level Security (CLS) integrated with LDAP/AD. • Headless and API security: OAuth, SAML, Personal Access Tokens, and Connected Apps (JWT). • Cross-border data sovereignty and regulatory isolation (China PIPL). • APIs & Developer Platform: • Expert-level REST API, Metadata API (GraphQL), and Embedding API v3. • VizQL Data Service / Headless Tableau for programmatic data extraction. • Robust automation scripting in Python or TypeScript/Node.js. • GenAI, Autonomous Agents & MCP: • Practical development and deployment of Model Context Protocol (MCP) servers. • Structuring BI semantic models as machine-readable context for LLMs. • Enforcing end-user identity and entitlement propagation through AI tool calls. Candidate Qualifications • Experience: 8–12+ years in Enterprise Data Engineering / Systems Architecture, with 5+ years dedicated to enterprise Tableau platform infrastructure and APIs. • Background: Proven track record in Big Tech, hyperscale SaaS, or Tableau Professional Services. • Scale: Verifiable experience ope rating clusters supporting >25,000 active users and leading legacy migrations (ThoughtSpot, BusinessObjects, HAWK). Vendor Technical Pre-Screening Questions (Candidates must provide written technical responses with resume submission:) 1. AI & MCP: How would you architect an MCP server that lets an AI agent query Tableau data while dynamically passing the user's identity to enforce Row-Level Security (RLS)? 2. Scalability: What caching, pooling, and workbook optimization strategies prevent cluster overload and ensure sub-second response times? Salary Range: $110,000-$150,000 a year #LI-AS3 </div

Requirements

Must-haves

Core Deliverables
Tableau Studio: Enterprise-wide rollout, lifecycle governance, self-serve onboarding, and creator community adoption.
Tableau Data Apps: Architecture and deployment of interactive, web-based analytics applications using modern embedding frameworks (Embedding API v3, VizQL Data Service).
Endor + Tableau (EA): Deep integration with Enterprise Assistant (EA) and autonomous BI agents; providing certified semantic grounding to eliminate LLM hallucinations while preserving dynamic user entitlements.
MCP Server Enablement: Production Model Context Protocol (MCP) pipelines exposing Tableau schemas, metadata, and query engines to developer and analyst AI workflows.
Required Technical Competencies
Multi-node cluster architecture and tuning under heavy peak concurrency.
Native integration and query performance optimization with modern engines: Trino, StarRocks, and Snowflake.
Workload optimization: Hyper extract caching, backgrounder scheduling, and query bottleneck analysis.
Dynamic Row-Level (RLS) and Column-Level Security (CLS) integrated with LDAP/AD.
Headless and API security: OAuth, SAML, Personal Access Tokens, and Connected Apps (JWT).
Cross-border data sovereignty and regulatory isolation (China PIPL).
Expert-level REST API, Metadata API (GraphQL), and Embedding API v3.
VizQL Data Service / Headless Tableau for programmatic data extraction.
Robust automation scripting in Python or TypeScript/Node.js.
Practical development and deployment of Model Context Protocol (MCP) servers.
Structuring BI semantic models as machine-readable context for LLMs.
Enforcing end-user identity and entitlement propagation through AI tool calls.
Candidate Qualifications
Experience: 8–12+ years in Enterprise Data Engineering / Systems Architecture, with 5+ years dedicated to enterprise Tableau platform infrastructure and APIs.
Background: Proven track record in Big Tech, hyperscale SaaS, or Tableau Professional Services.
Scale: Verifiable experience ope rating clusters supporting >25,000 active users and leading legacy migrations (ThoughtSpot, BusinessObjects, HAWK).
Vendor Technical Pre-Screening Questions
(Candidates must provide written technical responses with resume submission:)

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

apiaillmrestgraphqlpythontypescriptnode.js

Platform: careers-page
Effort: medium