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Senior Manager - Data & Analytics

Everpure · Posted Nov 2

Enterprise data storage and management platform for apps, analytics, and AI

Bangalore, IndiaFull-timeOnsiteSenior Level15+ years₹50.0L–₹90.0L yearly<25 applicants
Data StorageEnterprise Data CloudAI InfrastructureFlash StoragePublic Company
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About the role

Everpure is a data platform company (NYSE: P) that serves hyperscalers, AI labs, and the broader AI ecosystem. The Data & Analytics team owns the enterprise data, BI, and AI portfolio. This Senior Manager role leads a team of developers, analysts, and data scientists to deliver scalable, trusted, and widely adopted data products, working hands-on with SQL, Snowflake, Python, and BI tools.

What you will do

  • Lead the team. Manage a talented team of senior and junior developers, analysts, and data scientists — setting direction on best practices, engineering standards, and alignment with team priorities.
  • Develop people. Mentor team members, connecting their personal and professional goals to real career growth; set the standard for rigor and craft.
  • Be the trusted partner. Serve as the primary interface to the different organizations and run structured discovery to elicit underlying needs, not feature requests.
  • Write requirements that hold up. Translate business questions into testable specs: source systems, grain, metric definitions, SLAs, quality thresholds, and acceptance criteria. Drive a single source of truth for core metrics and arbitrate definitional conflicts.
  • Simplify the complex. Navigate ambiguous, politically loaded problems and distill them into actionable messaging that aligns with business priorities and lands with executives. Prioritization,
  • Own the roadmap. Manage, prioritize, and align data, reporting, and analytics requirements with business needs, overseeing development and implementation.
  • Make trade-offs explicit. Apply a consistent prioritization framework — value, reach, effort, risk, strategic fit — and communicate the rationale transparently, including to stakeholders you deprioritize.
  • Shape strategy at the table. Play a key role in cross-functional committees defining data mart strategy, governance, and policy; invest ahead of demand in capabilities that make future requests cheaper to serve.
  • Design for the decision. Set the standard for how analytics is presented — choose the right visual for the question, build a clear hierarchy, and design every dashboard around the decision the user is trying to make rather than the data that happens to be available.
  • Own the design system. Define and enforce visualization standards across the portfolio — chart conventions, color, typography, layout, naming, interaction patterns, and accessibility — so products look and behave consistently and stay maintainable as the team grows.
  • Prototype, test, and prune. Wireframe and iterate with real users before building; validate with usage data and feedback, and retire dashboards that don't drive action.
  • Lead technically. Hands-on in advanced SQL on Snowflake, with the credibility to review the team's work rather than take it on faith.
  • Design for scale. Oversee scalable pipelines and workflows that process large, diverse, unstructured datasets with high performance and reliability; automate cleaning, integration, and analysis in SQL and Python.
  • Enable self-service. Champion governed datasets, semantic layers, and intuitive dashboards that let non-technical users answer their own questions — over one-off extracts and bespoke reports.
  • Optimize what exists. Drive innovation in data architecture, dashboard performance, and cost efficiency; champion data quality and accuracy through lineage, testing, and clear ownership.
  • Set the AI agenda. Identify, size, and prioritize the use cases where ML and generative AI create real business value — and say no to the ones that don't.
  • Deliver GenAI experiences. Build generative AI into the analytics stack — natural-language querying, conversational BI, automated insight generation, AI-assisted documentation and discovery.
  • Measure what matters. Define success metrics for every product — adoption, time-to-insight, data freshness, decisions enabled, cost — and hold the team accountable to them.
  • Drive adoption and clarity. Lead enablement through documentation, training, and change management; present insights, risks, and trade-offs to audiences from individual engineers to the executive staff.

Skills used in this role

What the employer is looking for

  • 15+ years in analytics, business intelligence, data science, or data platform roles, including 4+ years directly managing teams. An advanced degree in statistics, data science, or computer science preferred.
  • Demonstrated ownership of an enterprise data, BI, or analytics portfolio from requirements through production adoption, with evidence of business impact — plus a track record of solving complex problems and using data to influence senior stakeholders.
  • Domain expertise in one or more of: finance, customer, or product. Experience with CRM and ERP datasets strongly preferred.
  • Advanced SQL — able to write, review, optimize, and debug complex analytical queries independently; deep experience with Snowflake or a comparable cloud data warehouse.
  • Knowledge of Python, including package development and production-quality code standards.
  • Hands-on with enterprise BI and semantic layer tools (Tableau, Power BI, Looker, ThoughtSpot) and what makes them perform at scale; working knowledge of governance, lineage, observability, and access control.
  • Strong data visualization craft — fluent in chart selection, visual hierarchy, and the principles that separate a clear view from a cluttered one. Able to take a dense, ambiguous dataset and present it so an executive understands it in seconds.
  • Genuine design sensibility: layout, whitespace, typography, purposeful color, and accessibility (including colorblind-safe palettes). A portfolio of dashboards or reports you designed is welcome.
  • Experience establishing visualization standards or a design system across a BI portfolio, and comfort wireframing and iterating with users. Familiarity with UX principles and design tools such as Figma is a plus.
  • Sophisticated stakeholder management: build consensus across functions, hold a position under pressure, and say no without damaging the relationship.
  • Connect data and AI investments to business outcomes and articulate ROI in the language of the function being served.
  • Excellent written and verbal communication — equally capable of a one-page decision memo and an executive presentation.

Benefits and support

  • flexible time off
  • wellness resources
  • company-sponsored team events

About Everpure

Everpure logo

Everpure

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Everpure is an enterprise data storage and management company that unifies all data with an enterprise data cloud, making it easy to manage and get AI-ready fast. Formerly known as Pure Storage, the company delivers an industry-leading, ever-evolving storage and data management platform.

Industry
Data Storage
Company size
6000+ employees
Founded
2009
Location
Santa Clara, California, USA
Funding stage
Public Company

Funding

Public Company · $470M raised

Sutter Hill VenturesGreylock PartnersRedpoint Ventures
  • 2013-08-29Series E$150M
  • 2014-04-23Series F$225M

Leadership

CG
Charles Giancarlo

Chairman and CEO

RL

Rob Lee

Chief Technology Officer

DF

Dan FitzSimons

Chief Revenue Officer

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