Power BI & AI Visualization Developer

We need to output HTML fragment with sections only if content present. Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content. The posting includes: About Us (company description) maybe not needed as heading? Could be "About the role" but we have "Summary" describing role. We'll treat that as About the role. Responsibilities: list of duties under "Essential Duties & Responsibilities". We'll output

• each bullet. Requirements: maybe Education & Experience? Could be split: Education & Experience as Requirements? Also Qualifications maybe same. But we need to follow headings list: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide what goes where. The posting includes Education & Experience (Bachelor's degree, 5-8 years, etc.) That's qualifications/requirements. Also there is a list of skills: Power BI, SQL, etc. Also there is "Experience migrating BI reporting..." etc. Those could be under Skills or Qualifications. We need to avoid duplication. We can put Education & Experience under Requirements. Skills under Skills. Qualifications maybe combine with Requirements? But we need separate headings only if content. Could put Qualifications empty -> omit. Better to put Requirements for education/experience, Skills for technical skills, Qualifications maybe for other qualifications like experience migrating, etc. But we have many bullet-like items. Let's parse: - Summary (About the role) - Essential Duties & Responsibilities (list) - Education & Experience (list of bullet-like sentences) - Travel Requirements (Travel up to 25%) - What We Offer (benefits list) - Diversity Commitment (boilerplate, remove) - Also mention hybrid schedule, location Dallas, TX. We have Schedule: hybrid schedule 2-3 days per week, located in Dallas, TX. Pay: not mentioned. Benefits: list medical, dental, vision, tuition reimbursement, paid parental leave, life accident disability, retirement with match, PTO. We need to output sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay (omit if none), Schedule. We need to decide what goes into Requirements vs Qualifications vs Skills. Maybe: - Requirements: Education & Experience (Bachelor's degree, 5-8 years experience, etc.) - Qualifications: Experience migrating BI reporting, experience administering Power BI, experience with source control, familiarity with tooling, direct experience productionalizing AI-based dashboard, experience building automation with Python, experience setting KPI standards, exposure to ETL/pipeline work. - Skills: Strong Power BI skills, Strong SQL skills, Strong communication skills, etc. But there is overlap. Let's extract explicit skill statements: From Education & Experience section: "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)." "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." "Experience 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." "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." Thus we can categorize: Requirements: Bachelor's degree, 5-8 years experience. Skills: Power BI, SQL, communication, etc. Qualifications: maybe the more specific experiences (migration, admin, source control, tooling, AI productionalization, Python automation, KPI governance, ETL exposure). But many of those are also skills. Given the instruction to prefer few substantive sections over many thin ones, we could combine Requirements and Qualifications into one? But headings are predefined; we can omit sections if no content. We could put all under Requirements and Skills, and omit Qualifications if we think it's redundant. However we need to include all substantive information; we can place the detailed experience bullets under Requirements or Skills. Let's decide: - About the role: summary paragraph. - Responsibilities: list of duties. - Requirements: Education & Experience (degree, years experience) plus maybe core mandatory skills (Power BI, SQL, communication). Could also include "Strong Power BI skills", "Strong SQL skills", "Strong communication skills". - Skills: maybe the additional technical skills/experience (migration, admin, source control, tooling, AI, Python, KPI governance, ETL). But that may be thin. Alternatively, we could put all experience/skill bullets under Skills, and keep Requirements just for education/experience. That seems reasonable. Thus: Requirements: Bachelor's degree in quantitative, business, or technical field or equivalent practical experience. 5-8 years of experience in analytics, business intelligence, or a related data role, including hands-on BI development. Skills: 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). 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. Experience 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. 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. Benefits: list. Schedule: hybrid schedule 2-3 days per week, located in Dallas, TX. Also mention travel up to 25% maybe under Schedule? Travel is separate but not a heading; we could include in Schedule paragraph. Pay: not mentioned, omit. Now format. We need to use

  for each section heading. Use

  • for any list of duties/requirements/benefits. For prose use

    . Thus:

    About the role

    ... summary paragraph ...

    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.

    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.

    Skills

    • 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).
    • 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.
    • Experience 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, T