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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Warehouse & Analytics Specialist - **Company:** Challenger School - **Location:** Sandy, UT, United States - **Salary:** $80,000.0 - $95,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Business Logic, Data Dictionary, Data Governance, Data Transformation, Data Structures, Data Warehousing, Relational Databases, Database Storage Structures, Digital Assets, Python (Programming Language), Meta-Data Management, Cloud Services, Standard Sql, Software Engineering, SQL Databases, Technical Data Management Systems, Snowflake, Data Lineage, Operational Systems, Tools for Reporting - **Published:** June 14, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=26cda02d4c91e02b ## About the Role Do you have experience in Technical writing?, * Strong SQL/PYTHON skills. * Experience working with relational databases, data warehouses, or cloud data platforms. * Ability to understand complex query logic and data transformations. * Strong analytical and problem-solving abilities. * Excellent written communication and documentation skills. * Strong attention to detail. * Ability to work effectively with technical and nontechnical stakeholders. Preferred Qualifications * Experience with Snowflake or similar cloud data warehouse platforms. * Business intelligence or reporting experience. * Experience documenting business rules, metrics, or data definitions. * Familiarity with semantic models, governed metrics, or metadata management. * Experience with Python for data analysis or validation. * Familiarity with AI-assisted analytics tools and workflows. Ideal Candidate The ideal candidate enjoys solving data puzzles, untangling complex business logic, identifying why reports disagree, and transforming technical information into clear business definitions. They are patient, precise, intellectually curious, and committed to data quality, consistency, and accuracy. ## Description Challenger School is seeking a Data Warehouse & Analytics Specialist to help build trustworthy, well-documented, AI-ready data assets that support reporting, analytics, research, and operational decision making. This role combines SQL/PYTHON analysis, data warehousing, semantic modeling, business intelligence, data governance, reporting validation, and AI-enabled analytics. The position serves as a bridge between technical systems and business users by translating database structures, reports, and metrics into clear, consistent business definitions. The ideal candidate enjoys working deeply with SQL/PYTHON, understanding how business metrics are calculated, documenting data definitions, resolving inconsistencies, validating reports, and helping create reliable data structures that can be used by analysts, business users, and AI systems. This position is heavily focused on SQL/PYTHON, documentation, data quality, warehouse analysis, semantic consistency, and business understanding rather than dashboard design or software engineering. Responsibilities * Analyze SQL/PYTHON queries, views, reports, source systems, and warehouse structures to understand how business metrics are calculated. * Trace data lineage from operational systems through data warehouse transformations, reporting layers, and semantic models. * Translate technical data logic into clear business definitions. * Develop and maintain business glossaries, data dictionaries, metric definitions, and metadata documentation. * Validate report outputs against source systems and warehouse data. * Support the development and maintenance of Snowflake semantic models. * Create and maintain mappings between business terminology and physical database structures. * Review and validate AI-generated SQL/PYTHON, analytics, and business insights. * Work with stakeholders to standardize business definitions and reporting logic. * Support data quality, reconciliation, governance, and AI-readiness initiatives. * Help prepare trusted data assets for natural-language querying, AI-assisted reporting, and future analytics initiatives. ## Related Videos - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Beyond SQL Generation: How to Teach Agents What Your Database Actually Means](https://www.wearedevelopers.com/videos/100127-beyond-sql-generation-how-to-teach-agents-what-your-database-actually-means) - [Making Data Warehouses fast. 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