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# Analytics Engineer

- **Company:** Commvault Systems, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $55,250.0 - $120,750.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Computer Vision, Microsoft Azure, Cloud Computing, Information Systems, Github, Python (Programming Language), Marketing Information Systems, Release Management, SQL Databases, Retrieval-Augmented Generation, Large Language Models, Data Lakes, Information Technology, Commvault, Databricks
- **Published:** October 12, 2026
- **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/data-analytics-engineer/11661219

## About the Role

· 2+ years of experience in analytics engineering, business intelligence, semantic modeling, data analysis, or related disciplines.

· Strong SQL skills and practical experience with dimensional modeling and analytical data structures.

· Hands-on experience with Power BI and/or Microsoft Fabric semantic models, including DAX.

· Experience designing and supporting enterprise reports, datasets, or semantic models across multiple subject areas.

· Understanding of data quality, metadata, lineage, access control, testing, and release management.

· Ability to translate business requirements into clear technical designs and communicate effectively with stakeholders.

· Bachelor's degree in Computer Science, Information Systems, Analytics, Engineering, or a related field; equivalent practical experience will be considered.

Preferred Skills & Experience

· Experience with Databricks, Delta Lake, medallion architecture, or dbt-style transformation practices.

· Experience with Microsoft Purview, Unity Catalog, Azure DevOps, or GitHub.

· Familiarity with AI-ready datasets, retrieval-augmented generation, or governed data consumption for AI applications.

· Experience supporting enterprise Finance, GTM, Product, Customer, or Workforce analytics domains.

You'll love working here because...

 * You'll contribute to a mission that matters by helping organizations around the world safeguard and recover their most valuable asset: their data.
 * You'll gain experience with modern analytics, cloud, and AI technologies that are shaping the future of enterprise data.
 * You'll collaborate with smart, supportive teammates who are passionate about innovation, curiosity, and solving complex challenges together.
 * You'll have visibility across the business and the opportunity to influence how data is understood, trusted, and used throughout the organization.
 * You'll be part of a culture that values flexibility, well-being, continuous learning, and helping employees do their best work while growing their careers.

## Description

Working across business domains, Data Engineering, Data Governance, and platform teams, this role translates business concepts into consistent data models and measures while ensuring analytical assets are secure, performant, documented, and supportable.

What You'll Do...

Semantic Modeling & Governed Metrics

· Design, build, test, and maintain semantic models using Microsoft Fabric and Power BI.

· Develop dimensional models, measures, KPIs, hierarchies, calculations, and reusable analytical components.

· Translate business definitions and reporting requirements into consistent, governed analytical models.

· Optimize model relationships, DAX calculations, refresh strategies, aggregations, and query performance.

· Apply naming, versioning, documentation, and change-control standards to semantic assets.

Analytics Engineering & Delivery

· Develop curated Gold-layer datasets and analytical views in collaboration with Data Engineering.

· Build and maintain reusable SQL transformations, validation routines, and data reconciliation controls.

· Support migration and modernization of existing reports and models with minimal disruption to business users.

· Participate in design reviews, testing, deployment, incident resolution, and production support.

Data Governance & AI Readiness

· Document business definitions, ownership, metadata, lineage, quality expectations, and approved usage.

· Partner with Data Governance to align semantic assets with glossary terms, classifications, access policies, and certification processes.

· Prepare trusted datasets and metrics for predictive analytics, generative AI, copilots, agents, and retrieval use cases.

· Help ensure AI solutions consume authoritative data sources and approved enterprise metrics.

Stakeholder Partnership & Continuous Improvement

· Work with Finance, GTM, Product, Customer, Workforce, and other domain stakeholders to clarify analytical needs.

· Explain model behavior, data limitations, and metric logic to technical and nontechnical audiences.

· Create reusable patterns, technical documentation, and knowledge-sharing materials.

· Identify opportunities to simplify models, improve user experience, increase reuse, and reduce reporting duplication., Architects and owns marketing data infrastructure, including Databricks pipelines, lakehouse tables, semantic layers, and data models. Leads cross-functional data initiatives, ensures data quality and reliability, mentors engineers, and translates ambiguous business needs into scalable solutions. Builds, evaluates, and monitors production LLM and agent systems, automates marketing and go-to-market workflows with AI, and delivers high-quality Python and SQL solutions.
Top Skills: Ai AgentsBigQueryClaude CodeCursorDatabricksDbtLlmsPythonSalesforceSnowflakeSQLStreamlit, Samsara