Senior Analytics Engineer, GTM

Everpure•Santa Clara, CA

17h•Onsite

About The Position

We’re in an unbelievably exciting area of tech and are fundamentally reshaping the data storage industry. Here, you lead with innovative thinking, grow along with us, and join the smartest team in the industry. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. 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.

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

Nice To Haves

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

Responsibilities

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