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# Tableau Data Engineer & Modeler

- **Company:** TechInsights Inc.
- **Location:** Denver, CO, United States
- **Salary:** $86,275 - $91,350
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon S3, Business Logic, Business Intelligence Development, Code Review, Data as a Services, Data Dictionary, Information Engineering, Digital Assets, Python (Programming Language), PostgreSQL, Metadata, Operational Databases, Power BI, Salesforce.Com, Tableau (Software), Sql Optimization, Large Language Models, Agentic-AI, Git, Microsoft Fabric, Data Analytics, Star Schema, Data Management, Virtual Agents, Restful APIs, Model Context Protocol, Code Restructuring
- **Published:** November 7, 2026
- **Apply:** http://techinsights.applytojob.com/apply/jobs/details/I6ZoEBBOzy

## About the Role

* 4+ years in data engineering, analytics engineering or BI development, including 2+ years building production Tableau data sources
 * Have built and maintained at least 3 production Tableau Prep flows that run on a schedule and use complex joins, unions, pivots and aggregations
 * Have published at least 5 Tableau data sources used across multiple dashboards or teams, with clear decisions on relationships vs. joins and extracts vs. live connections
 * 3+ years writing advanced SQL, ideally PostgreSQL, including window functions, CTEs and materialized views. You can show at least one query you improved by reading its query plan.
 * Have designed at least one dimensional model (star schema) end to end, covering grain, slowly changing dimensions, conformed dimensions and many-to-many relationships
 * Have reconciled data models or reports to financial or system-of-record figures at least twice, documenting differences and getting stakeholder sign-off
 * Have set up row-level security or user-based access controls in Tableau for at least one production data source
 * 1+ year working with Salesforce data as a source, including objects, relationships and history tables
 * Can explain how the Model Context Protocol (MCP) and the Tableau MCP server let AI agents read metadata and query data sources, and name at least 3 model features that make agent answers more or less accurate
 * Can share 2+ examples of technical documentation you wrote, such as data dictionaries, lineage notes or design decisions, for both technical and non-technical readers

Preferred

 * A current Tableau certification, such as Tableau Certified Data Analyst or Tableau Certified Architect
 * Hands-on use of at least 2 of these: Tableau Pulse, Tableau Catalog / Data Management, VizQL Data Service, Tableau REST API or Metadata API
 * Have tested LLM or AI agent access to BI data and measured how accurate the answers were against a known source
 * 1+ year using Python for automation, API work or Tableau Prep scripting steps
 * Experience with AWS data services (RDS, S3, Lambda) and Git version control for data assets
 * Experience with at least one other semantic layer or BI tool, such as Power BI / Microsoft Fabric or dbt

## Description

* Design certified, reusable published data sources that become the single source of truth for core business entities, using validated business logic and the right grain for each use case.
 * Standardize calculated fields, LOD expressions and hierarchies at the data-source level so dashboards stay consistent.
 * Tune extracts and live connections for performance and refresh cost through aggregation, filtering, incremental refresh and field pruning.
 * Prepare models for AI agents using the Tableau MCP server. This means writing clear field names, captions, descriptions and metadata, and removing duplicate fields, unclear measures and inconsistent date logic.
 * Test agent queries against our models, find where answers go wrong, and fix the model rather than the prompt.
 * Help define Tableau Pulse metrics and the semantic layer that both people and agents rely on.
 * Check every new or changed model and report against Finance-approved figures or the source system of record before release. Nothing goes live until differences are explained, documented and signed off by the data owner.
 * Build automated data quality checks between source systems and Tableau, and flag process failures to data owners.
 * Maintain lineage and documentation in Tableau Catalog / Data Management, and set up row-level security, user filters and permissions that follow our governance framework.
 * Build, schedule and monitor Tableau Prep flows from Salesforce, PostgreSQL and other sources. You'll own run reliability, failure alerts and recovery.
 * Refactor older flows and extracts so they're faster, easier to maintain and cheaper to refresh.
 * Work with Analysts, Sales, Finance and Salesforce admins to turn reporting needs into durable models, and train Tableau authors through documentation and code review.