Data & Analytics Lead (Quantitative / Data Modeling Focus)- 12 Month Contr at Pure Storage

Staines-upon-Thames, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

02 May, 26

Salary

0.0

Posted On

01 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Modeling, Data Architecture, Quantitative Analysis, SQL, Python, R, BI Tools, Data Governance, Collaboration, Predictive Analytics, Prescriptive Analytics, Benchmarking, Regression Analysis, Customer Data Mapping, Insight Generation, Data Confidence Scoring

Industry

IT Services and IT Consulting

Description

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 ROLE 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. The successful candidate combines strong data architecture and quantitative analysis skills with the ability to translate complex data into actionable business insights. WHAT YOU'LL DO Data Model & Architecture 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. Analytics & Insight Generation 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. Customer & Opportunity Modeling 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. WHAT YOU BRING 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. We are primarily an in-office environment and therefore, you will be expected to work from the Staines and / or London office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave. Preferred Experience Background in enterprise data architecture or quantitative strategy consulting. Familiarity with Snowflake, Salesforce, and marketing intelligence or industry insights platforms (6Sense, Leadspace, HGInsights, etc.). Experience with regression modeling, clustering, and multivariate analytics. Understanding of financial modeling, cost analysis, and performance benchmarking. Master Data Role – Detail Key Objectives Collation 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. Analytics 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. Customer Opportunity Sizing, Validation, and Testing 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. We are primarily an in-office environment and therefore, you will be expected to work from the Staines office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave. #LI-ONSITE WHAT YOU CAN EXPECT FROM US: Pure Innovation: We celebrate those who think critically, like a challenge and aspire to be trailblazers. Pure Growth: We give you the space and support to grow along with us and to contribute to something meaningful. We have been Named Fortune's Best Large Workplaces in the Bay Area™, Fortune's Best Workplaces for Millennials™ and certified as a Great Place to Work®! Pure Team: We build each other up and set aside ego for the greater good. And because we understand the value of bringing your full and best self to work, we offer a variety of perks to manage a healthy balance, including flexible time off, wellness resources and company-sponsored team events. Check out purebenefits.com for more information. ACCOMMODATIONS AND ACCESSIBILITY: Candidates with disabilities may request accommodations for all aspects of our hiring process. For more on this, contact us at TA-Ops@purestorage.com if you’re invited to an interview. OUR COMMITMENT TO A STRONG AND INCLUSIVE TEAM: We’re forging a future where everyone finds their rightful place and where every voice matters. Where uniqueness isn’t just accepted but embraced. That’s why we are committed to fostering the growth and development of every person, cultivating a sense of community through our Employee Resource Groups and advocating for inclusive leadership. Pure is proud to be an equal opportunity and affirmative action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other characteristic legally protected by the laws of the jurisdiction in which you are being considered for hire. JOIN US AND BRING YOUR BEST. BRING YOUR BOLD. BRING YOUR FLASH.

Responsibilities

The Data & Analytics Lead will design and operationalize a unified data framework integrating financial, operational, and technical data. This role involves building analytical foundations that link data insights to customer performance and value realization.