Data & Analytics Lead, Pure Storage

• The successful candidate combines strong data architecture and quantitative analysis skills with the ability to translate complex data into actionable business insights
• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, Economics, or a related quantitative field
• 1+ years’ experience in data analytics, data modeling, or quantitative analysis, ideally in a B2B or enterprise technology environment
• Proficiency in SQL, Python/R, and BI tools (Tableau, Power BI, or similar)
• Experience designing data models and pipelines across multi-source systems (for example, CRM, Marketing Ops, Financial Systems)
• Strong understanding of data governance, normalization, and confidence scoring techniques
• Demonstrated ability to synthesise large, complex datasets into actionable insights
• Excellent collaboration skills with cross-functional teams including Sales, Finance, and Operations

Desirable

• We’re seeking a Data & Analytics Lead with a focus on quantitative analysis and data modeling to design and operationalize a unified data framework that integrates financial, operational, and technical data across multiple systems
• This role sits within the Global Value Management (GVM) team—an organisation focused on developing proactive, data-driven proposals and value propositions that help customers understand the business impact of our solutions
• This person will build the analytical foundation that powers GVM engagements, linking data insights to customer performance, opportunity sizing, and value realisation
• Define a master data model spanning financial, operational, and technical dimensions, including relationships and dependencies
• Collaborate with Sales Operations to determine the right platform and integration architecture
• Source and align data from multiple systems—Customer, Competitor, GVM Engagements, HGInsights, Marketing Ops (6Sense, Leadspace, Gartner, IDC), AlphaSense, and Snowflake
• Develop a data confidence scoring model (validated, inferred, assumed) and processes for maintenance, expiry, and refresh
• Define clear data governance rules, including ownership, quality standards, refresh cycles, and version control
• Build relational data sets linking metrics such as $/TB and FTE/TB
• Produce benchmarks, quartiles, and regression analyses to uncover performance drivers across cost, efficiency, and technical spread
• Design outputs that highlight “best-in-class” performance by vertical or environment (Cloud vs On-Prem)
• Create searchable internal indices for GVM use cases (for example, where similar takeouts or use cases exist)
• Deliver insight models that validate assumptions, expose trends, and inform customer recommendations
• Apply predictive and prescriptive analytics to recommend likely values and optimal ranges for missing or uncertain inputs
• Embed the master data model into core workflows and provide interfaces so models can pull and refresh data in real time
• Correlate customer data against the master model to assess confidence and identify gaps
• Use analytics to infer likely ranges for missing data and map customers to best-in-class benchmarks
• Load validated data into business case models to inform account planning and opportunity prioritization
• What Success Looks Like
• A reliable, scalable master data framework that informs GVM and customer strategy and serves as a single source of truth
• Automated confidence scoring and refresh processes
• Predictive and prescriptive analytics that estimate key values, recommend likely ranges, and guide opportunity sizing and customer value realization
• Benchmarking frameworks that inform strategic decisions and account planning
• A foundation for evidence-based, data-driven customer proposals
• We are primarily an in-office environment and therefore, you will be expected to work from the {{OFFICE_LOCATION}} office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave
• Define a data model of key types (tables) and data features (attributes) to collect, spanning financial, operational, and technical scope
• Define the data dependencies and relationships between them
• Partner with Sales Operations to determine the platform this should be built on
• Source multiple potential data records, including:
• Customer
• Competitor
• Account team
• GVM (Engagements)
• HGInsights
• Marketing Ops data (6Sense, Leadspace, Gartner, IDC, etc.)
• Past Value and Install Base (Skyline)
• Win/Loss
• AlphaSense
• Other data in Snowflake
• Map data sources to the master data model
• Define and build a confidence factor rating on the data (customer validated vs market data vs assumption)
• Define a model to maintain and update data sources
• Set expiry dates on data
• Build relational tables that index data sources based on their dependencies (for example, $/TB, FTE/TB)
• Produce quartiles and ranges of confidence factors based on indexed data sources
• Design and build insight reports that correlate attributes to performance, for example:
• Overall cost related to technical spread (size, utilisation, etc.) and operational costs/performance
• Operational efficiency relative to cost
• Technical debt relative to cost and operational cost/performance
• Cloud vs On-Prem
• Locations/FTEs per scope or location
• Design outputs to show “best-in-class” operating, technical, and financial performance attributes, with variation by vertical industry
• Create a searchable internal index for the GVM team (for example, where a PowerFlex takeout has been done previously)
• Build a referenceable index (for example, $/TB, FTE/TB in a given industry based on historical GVM experience)
• Develop benchmarking to provide ranges and comparisons across data sets to assess whether assumptions are reasonable
• Use regression analysis to identify trends in the data and uncover patterns that were not previously considered
• Map available customer data (direct or indirect) to the master data model
• Produce analytics to validate customer data against the master data and map to confidence factors (for example, whether known size and cost are within the range of master data sources)
• Use predictive and prescriptive analytics to recommend likely ranges of values for missing data based on master record dependencies (for example, for 5 FTEs in Operations, costs are likely in range X–Y)
• Produce analytical outputs that map the customer to a best-in-class model
• Load financials into the as-is business case model
• Embed the master data model into GVM workflows, with standardised interfaces and query access so models can pull, refresh, analyse and retrieve data in real time
• Use these insights to inform account planning, including estimating opportunity size and identifying high-potential opportunities