Senior Analytics Engineer (GTM)

Requirements 7+ years of experience in analytics engineering, data engineering, or business intelligence Strong expertise in SQL, Python, Snowflake, and modern ELT development practices Hands-on experience with dbt, orchestration workflows, and scalable data modeling Experience implementing CI/CD, automated testing, observability, and data quality frameworks Strong understanding of semantic modeling, metrics layers, governed metrics, and reusable analytics datasets Experience using AI to build workflows, automate processes, improve operational efficiency, or support business intelligence use cases Ability to influence technical direction while remaining hands-on Strong communication and collaboration skills across technical and business teams Pragmatic interest in applying AI capabilities to real business workflows over time Passion for modernization, reporting trust, and scalable analytics engineering

• Desirable) GTM, Sales, Revenue Operations, or forecasting analytics domains
• Desirable) Salesforce, Clari, Anaplan, Workday, or related enterprise systems
• Desirable) Analytics engineering modernization initiatives
• Desirable) Self-service analytics ecosystems and semantic layers
• Desirable) Conversational analytics, AI copilots, or workflow automation concepts
• Desirable) Experience migrating legacy SQL or ETL frameworks into modern dbt-based transformation workflows
• Desirable) Tableau or modern BI visualization platforms

What the job involves The GTM BI team at Pure Storage supports Revenue Operations across pipeline, forecasting, seller performance, and executive reporting — and we are at an inflection point We are midway through a strategic migration to dbt as our transformation standard. Beyond that, our roadmap includes scalable automation frameworks, semantic business intelligence, and AI-enabled workflows that help surface insights with less manual effort As a Senior Analytics Engineer, you will help drive that journey end-to-end. This role is grounded in modernization first: building scalable dbt foundations, improving quality and engineering standards, and enabling the team to operate more efficiently before scaling broader AI-enabled capabilities You will partner closely with BI analysts, Sales Operations, Systems teams, and backend engineering partners to modernize how analytics is engineered, governed, and consumed across the organization. This role is for someone who wants to build — not maintain Lead and contribute hands-on to the team’s dbt and Snowflake modernization initiatives Build scalable semantic and metrics layers supporting GTM domains including pipeline, forecasting, quota, attainment, and seller performance Design and maintain robust ELT/ETL pipelines, orchestration workflows, and reusable business-facing datasets Build CI/CD, testing, observability, lineage, and data quality frameworks for analytics engineering workflows Ingest and transform data from APIs and GTM systems into curated canonical datasets with strong quality standards Drive architecture decisions and establish modern analytics engineering best practices across the team Partner closely with BIAs, RevOps, and Systems teams to translate business problems into scalable data products Help automate manual reporting and operational workflows through configurable and scalable frameworks Apply AI capabilities to workflow automation, operational efficiency, and insight generation, while helping lay the foundation for future conversational analytics initiatives #J-18808-Ljbffr Pure Storage