Senior Manager - Data & Analytics

Everpure · Bangalore, India

  • Senior
  • Full-time
  • Posted 2026-09-11
  • Confirmed live on 25 September 2026

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Job description

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem.

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 are looking for a hands-on leader to own the data, analytics, and AI portfolio end to end — and the team that builds it. You are the single point of accountability between business stakeholders and the analytics organization: gathering and pressure-testing requirements, turning ambiguous problems into a prioritized roadmap, and leading the developers, analysts, and data scientists who deliver scalable, trusted, widely adopted products. This is not a role that manages from a distance. You will be in the requirements sessions, the data model reviews, the SQL, the dashboard design critiques, and the model evaluations — and you will be measured on adoption and business outcomes, not backlog size or model sophistication.

WHAT YOU'LL DO

Team Leadership & Talent
● 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.
Stakeholder Partnership & Requirements
● 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,

Roadmap & Datamart Strategy
● 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.

Visualization & Experience Design
● 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.

Technical Leadership: Warehouse, BI & Pipelines
● 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.

Data Science, AI & Machine Learning
● 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.

Adoption, Measurement & Communication
● Measure what matters. Define success metrics for every product — adopti

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