Senior Data Analytics Engineer

Recruitment Fraud Alert

We’ve learned that scammers are impersonating Commvault team members—including HR and leadership—via email or text. These bad actors may conduct fake interviews and ask for personal information, such as your social security number.  

What to know:

• Commvault does not conduct interviews by email or text.
• We will never ask you to submit sensitive documents (including banking information, SSN, etc) before your first day.

If you suspect a recruiting scam, please contact us at wwrecruitingteam@commvault.com 

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 company’s 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 100,000 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 you’ll 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…

• Bachelor's 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.

#LI-VK