Responsibilities
- Data Platform & Infrastructure - Own the Snowflake architecture, ingestion pipelines, and data reliability SLAs across all business functions.
- Define and enforce pipeline standards, data quality monitoring, and incident response so every function can trust the data they work with.
- Partner with Engineering to maintain clean, documented data contracts between source systems (Gainsight CS, Salesforce, RevPro, NetSuite, Workday, Ramp) and the warehouse.
- Build toward a self-service analytics environment where business teams can access certified data without waiting on a central queue.
- Semantic Layer & Metric Governance - Own all certified data models and the company’s canonical metric definitions - the authoritative source for ARR, NRR, churn rate, pipeline, health score, headcount, and all other business-critical KPIs.
- Facilitate the Data Council: the cross-functional governance body where CS, Sales, GTM, Finance, and Engineering align on definitions. When teams disagree on what a number means, the Data Council decides - and the outcome is encoded in code, not a slide deck.
- Build and maintain the data catalog - the living registry of every certified metric, its definition, source system, owner, refresh cadence, and change history.
- Drive data literacy across the organization so business teams know how to find, interpret, and trust the data available to them.
- Cross-Functional Domain Analytics - Lead domain analysts embedded across CS, Sales, GTM, and Finance - people who sit with their business teams and translate function-specific needs into solutions built on the central platform.
- For Customer Success: a reproducible, certified health score model, automated QBR data packages, and churn signal reporting that CSMs actually rely on.
- For Finance: clean automated pipelines from RevPro, NetSuite, etc. that eliminate manual close reconciliation and give the Finance a trusted month-end workflow.
- For Sales & GTM: reliable pipeline, funnel, and attribution data so RevOps and GTM leadership can run forecasting and planning from a single source.
- Prioritize domain coverage in partnership with the Chief AI & Transformation Officer based on where data gaps are causing the most business impact.
- Team & Culture - Hire and develop the Enterprise Data & Analytics org: platform engineers, analytics engineers, a governance manager, and domain analysts.
- Operate a federated model: the central team sets standards, domain analysts execute within them - neither a pure ivory tower nor a fully decentralized free-for-all.
- Create the conditions for data to be a shared organizational capability, not a scarce resource controlled by one team.
Requirements
- 12+ years in data, analytics, or data engineering, with at least 5 years leading multi-disciplinary data teams.
- Proven experience building or standardizing a data platform across multiple business functions in a SaaS environment - not just maintaining one someone else built.
- Strong hands-on fluency with the modern data stack: Snowflake, dbt, a pipeline orchestration tool (Fivetran, Airflow, or equivalent), and at least one BI platform (Sigma, Looker, Tableau, or similar).
- Experience owning a semantic layer and driving cross-functional alignment on metric definitions — you have brought Finance, Sales, and CS stakeholders to a shared agreement on business-critical KPIs like ARR, NRR, and churn, and ensured that alignment is encoded in governed, production-ready data models rather than remaining an informal understanding.
- Working knowledge of data governance: cataloging, lineage, access controls, data quality frameworks, and how to make governance feel like enablement rather than bureaucracy.
- Demonstrated ability to build credibility with non-technical business leaders across CS, Finance, Sales, or GTM and translate data platform capabilities into outcomes they care about.
- Experience hiring and developing data talent across engineering, analytics, and governance disciplines.
- Demonstrated curiosity and practical experience applying AI and LLM-based tools to accelerate data workflows - whether automating pipeline documentation, enabling natural language querying across the semantic layer, or surfacing anomalies and data quality issues without manual intervention.
Nice to Have
- Hands-on familiarity with the Gainsight data stack (e.g., Gainsight CS, Salesforce, RevPro, NetSuite) and data catalog platforms (e.g., Atlan, Alation, or Collibra).
- Experience operating in federated or hub-and-spoke data models across multiple business domains (e.g., supporting both CS/RevOps and Finance/FP&A).
- Exposure to ML/AI applications in a SaaS context (e.g., predictive churn, revenue forecasting) and experience driving company-wide data literacy or self-service analytics programs.
Work Arrangement
Hybrid — Hyderabad, India
Additional Information
- This role may require occasional travel (up to 20%) for team meetings, training, or company events.
- This is a full-time role.
- Hybrid role based out of Hyderabad, India.
