Power BI & AI Visualization Developer

Freeman•Dallas, TX

4d•Hybrid

About The Position

The Enterprise Data Analytics team is the central source of truth for performance reporting across Sales, Finance, Operations, and Customer Experience (CX). We're looking for a Power BI / AI Visualization Developer who can turn fragmented, multi-source data into clear, trusted dashboards used across the business — and who is ready to push our reporting stack beyond static dashboards into AI-assisted and AI-generated visualization tools. This role sits at the intersection of BI development, data engineering fluency, and applied AI. You'll build and maintain the enterprise's core Power BI reporting layer, define KPI and data governance standards across functions, and lead the build-out of AI-powered analytics tools — including productionalizing AI-driven dashboards and automation that read directly from our Snowflake warehouse. This position will support our Technology Services team. It is eligible to work a hybrid schedule, generally requiring work in-office and/or show-site 2-3 days per week. The ideal candidate will be located in Dallas, TX.

Requirements

Bachelor's degree in a quantitative, business, or technical field (e.g., Analytics, Statistics, Business, Computer Science) or equivalent practical experience.
5- 8 years of experience in analytics, business intelligence, or a related data role, including hands-on BI development.
Strong Power BI skills: data modeling, DAX, Power Query/M, and report/dashboard design.
Strong SQL skills and experience working against a cloud data warehouse (Snowflake, BigQuery, Redshift, or similar).
Demonstrated experience turning multi-source, messy, or inconsistent data into a single reliable reporting layer.
Strong communication skills — able to listen closely, ask the right clarifying questions, and clearly explain technical work to non-technical stakeholders before and after building a solution.
Experience migrating BI reporting from one cloud data warehouse to another (e.g., Azure to Snowflake), including rebuilding data models and validating parity with legacy reports.
Direct experience productionalizing an AI-based dashboard or reporting tool that reads from Snowflake (or a comparable warehouse) — e.g., an LLM-powered insights layer, natural-language-to-SQL interface, automated narrative/report generation, or similar — taken from prototype to a stable tool used by a team.
Experience building automation or internal tools with Python (e.g., Streamlit, or similar lightweight app frameworks).
Experience setting or governing measurement/attribution or KPI standards across multiple teams or business units.
Some exposure to ETL/data pipeline work (e.g., Stitch, Fivetran, dbt, or custom pipelines) in addition to reporting.

Nice To Haves

Exposure to administering Power BI at an enterprise level — workspace/access management, row-level security (RLS), gateways, and refresh scheduling — is a plus.
Experience keeping semantic models under source control and deploying them through a repeatable process (Git integration, TMDL, deployment pipelines).
Familiarity with the external tooling ecosystem for semantic models — Tabular Editor, DAX Studio, ALM Toolkit, and Best Practice Analyzer.

Responsibilities

Design, build, and maintain Power BI dashboards and semantic models that serve Sales, Finance, Operations, and CX stakeholders with a single, consistent view of performance.
Enable self-service analytics for business users, and use embedded analytics and data-driven alerts where they get insight in front of stakeholders faster than a dashboard visit.
Lead the rebuild of existing schemas, semantic models, and Power BI reports currently served from Azure data views onto Snowflake, as part of the team's migration off Azure — re-mapping data models and validating that rebuilt reports match legacy output before cutover.
Meet with stakeholders to understand the actual business question before building — translating loosely defined asks into clear requirements, rather than jumping straight to a dashboard.
Establish and enforce KPI definitions, data governance, and QA standards so metrics are calculated consistently across teams and brands/business units.
Create and maintain documentation for data sources, semantic models, KPI definitions, and Power BI solutions, so institutional knowledge does not live in one person’s head.
Write and optimize SQL against a Snowflake (or similar cloud warehouse) environment, building views and data models that feed BI tools cleanly and efficiently.
Optimize the performance and efficiency of Power BI reports, queries, and semantic models — model design, DAX tuning, and incremental refresh — so large models stay responsive as data volumes grow.
Design and productionalize AI-assisted or AI-generated reporting tools — for example, LLM-powered summarization of dashboard results, natural-language query layers over Snowflake data, or automated deck/report generation — moving them from prototype to a reliable, team-wide production tool.
Audit and validate dashboard and data model inputs by hand when outputs look off, tracing discrepancies back to source data or logic errors rather than taking vendor or pipeline outputs at face value.
Build lightweight internal tools (e.g., Python/Streamlit apps) that automate recurring, manual reporting cycles and cut turnaround time for the team.
Own recurring reporting cadences (weekly/monthly, quarterly business reviews) with a consistent, repeatable production process.
Evaluate new data sources and analysis methods, recommending the right approach for the right decision (e.g., trend/diagnostic reporting vs. deeper analysis).
Help administer the Power BI environment — workspace and report access, row-level security (RLS) so users see only the data appropriate to their role, and gateway/data source connections and refresh schedules — working alongside the current workspace admin.
Mentor junior analysts on Power BI, SQL, and data hygiene best practices as the team grows.