Senior Data Analytics Engineer

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About Commvault

Commvault (NASDAQ: CVLT) is the gold standard in cyber resilience. The company empowers customers to uncover take action and rapidly recover from cyberattacks keeping data safe and businesses resilient. The companys unique AI-powered platform combines best-in-class data protection exceptional data security advanced data intelligence and lightning-fast recovery across any workload or cloud at the lowest TCO. For over 25 years more than 100000 organizations and a vast partner ecosystem have relied on Commvault to reduce risks improve governance and do more with data.

The Senior Data Analytics Engineer is responsible for designing developing and operating governed analytics semantic models reusable business metrics and AI-ready analytical assets. This role combines analytics engineering semantic modeling business intelligence data quality and practical AI enablement to deliver trusted data products that support reporting decision-making self-service analytics and approved AI use cases.

The position partners closely with Data Engineering Data Governance Data Science business analysts and application teams to translate business definitions source-system context and analytical requirements into scalable semantic models and governed consumption layers. The ideal candidate is a hands-on senior individual contributor with deep SQL BI semantic modeling and analytics engineering expertise along with working knowledge of data science knowledge graphs retrieval-augmented generation and AI-ready data patterns.

What youll do

Analytics Engineering Semantic Models & Data Products

• Design build test and maintain semantic models dimensional models curated datasets measures KPIs hierarchies and reusable business logic.
• Develop enterprise analytics solutions using SQL Power BI Microsoft Fabric Databricks and approved cloud services.
• Consume governed Gold-layer data and work with Data Engineers to resolve modeling quality performance and integration issues.
• Optimize semantic models and analytical queries for usability scalability refresh performance and secure access.
• Support dashboard developers analysts and self-service users with well-documented analytical assets.
  

Applied Data Science & AI Readiness

• Apply working knowledge of statistical methods machine learning concepts and AI patterns to design analytics assets that can support downstream data science and AI use cases.
• Partner with AI and Engineering teams to understand modeling feature evaluation and retrieval requirements and translate them into reliable analytical datasets and reusable data products.
• Develop curated feature-ready datasets dimensional models and semantic structures that support forecasting segmentation classification anomaly detection and other approved analytical use cases.
• Support generative AI and RAG solutions by preparing high-quality business definitions metadata retrieval-ready content embeddings inputs and governed analytical context.
• Contribute to knowledge graph and ontology-aligned data structures that connect business concepts metrics entities relationships source systems and governed data assets.
• Perform exploratory analysis profiling reconciliation and validation to confirm that analytics and AI-ready assets are accurate explainable and fit for business consumption.
• Communicate findings data limitations modeling assumptions and recommended actions to business technical and governance stakeholders.

AI Engineering & MLOps Delivery

• Build and maintain deployment pipelines for machine learning and generative AI workloads using version control automated testing and CI/CD practices.
• Support experiment tracking model registration release management batch or real-time inference monitoring drift detection and operational troubleshooting.
• Develop reusable feature datasets evaluation datasets retrieval pipelines embeddings and vectorized knowledge assets for approved AI use cases.
• Implement automated tests for data semantic models model artifacts prompts retrieval quality and production workflows.
• Collaborate with Data Engineering teams to productionize machine learning solutions through scalable data pipelines and MLOps frameworks.
• Partner with Data Scientists and platform teams to improve reliability observability security responsible AI controls and cost management.

Governance Quality & Documentation

• Document business definitions calculations data sources model dependencies ownership and operational procedures.
• Partner with Data Governance on glossary alignment metadata lineage classification access controls and quality expectations.
• Perform data profiling reconciliation root-cause analysis and issue remediation across analytics and AI workflows.
• Ensure analytical and AI assets follow established architecture privacy security and responsible AI standards.

Business Partnership & Team Contribution

• Translate business questions and AI use-case requirements into practical technical designs and delivery plans.
• Collaborate with cross-functional teams across Finance GTM Product Customer People and other enterprise domains.
• Present technical findings analytical results risks and recommendations clearly to business and technical audiences.
• Mentor analysts and engineers on semantic modeling SQL testing deployment and operational best practices.
• Contribute reusable code patterns documentation and lessons learned to the Analytics & Semantics capability.
  

Who you are

• Bachelors degree in Computer Science Engineering Information Systems Data Analytics Data Science Mathematics Statistics or related quantitative or technical field.
• Minimum of 5 years of professional experience in analytics engineering business intelligence semantic modeling data engineering data analytics or related discipline.
• Advanced SQL experience including complex query development performance optimization data profiling reconciliation and analysis across large-scale enterprise datasets.
• Experience designing and maintaining semantic models dimensional models metrics layers KPIs hierarchies relationships reusable calculations and governed business logic.
• Hands-on experience developing analytics solutions using Power BI Microsoft Fabric Databricks Spark Python or comparable modern data and analytics platforms.
• Experience building curated analytical datasets feature-ready data assets and governed consumption layers that support reporting self-service analytics data science and AI use cases.
• Working knowledge of statistical analysis machine learning concepts feature engineering model evaluation and common data science use cases such as forecasting segmentation classification anomaly detection and recommendation.
• Working knowledge of generative AI and retrieval patterns including large language models embeddings vector search retrieval-augmented generation prompt evaluation and AI agents.
• Familiarity with knowledge graph ontology taxonomy metadata lineage glossary and entity-relationship concepts used to connect business meaning with governed data assets.
• Experience implementing data quality checks validation routines testing practices documentation standards source-to-target mapping and operational controls for analytics and semantic assets.
• Experience partnering with cross-functional stakeholders to translate business definitions source-system context reporting needs and AI requirements into scalable technical designs.
• Strong written and verbal communication skills with the ability to explain metrics data lineage data quality findings analytical logic risks and recommendations to business technical and leadership audiences.
  

Preferred Skills & Experience

• Experience with Power BI semantic models DAX Microsoft Fabric Databricks SQL Unity Catalog Microsoft Purview or comparable data governance and analytics platforms.
• Experience developing business intelligence dashboards and analytics products; Power BI experience is preferred and experience with Tableau Qlik or comparable tools is also acceptable.
• Knowledge of enterprise business applications such as Salesforce NetSuite Marketo Workday or comparable CRM ERP marketing automation and HR systems is strongly preferred.
• Experience supporting knowledge graph ontology taxonomy business glossary entity resolution or semantic layer initiatives.
• Experience preparing retrieval-ready data metadata embeddings inputs vector search assets or governed context for RAG and enterprise AI solutions.
• Experience working in an Agile product or platform delivery model with geographically distributed teams.

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