Director, Enterprise Data & Analytics

Job description

We’re building the AI-driven future of customer success, from retention to growth! We’re building the AI-driven future of customer success, from retention to growth! Gainsight is the AI-powered retention engine behind the world’s most customer-centric companies. The Gainsight CustomerOS platform orchestrates the customer journey from onboarding to outcomes to advocacy. More than 2,000 companies trust Gainsight’s applications and AI agents to drive learning, adoption, community connection, and success for their customers. To explore how our suite of solutions is shaping the future of customer success, check out the link https://www.gainsight.com. About This Role: We’re looking for a full-time Director, Enterprise Data & Analytics to join our Engineering team reporting to the Chief AI & Transformation Officer . This role is a hybrid role based out of our Hyderabad, India. In this role, you'll play a key role in giving every function at Gainsight a single, trusted source of data by owning the platform, pipelines, semantic layer, and domain analysts that turn raw data into decisions. This is a great opportunity for someone who thrives in a fast-moving, cross-functional build environment and enjoys working cross-functionally with teams like Customer Success, Sales, GTM, Finance, and Engineering. The ideal candidate brings strong skills in modern data stack architecture (Snowflake, dbt, pipeline orchestration), semantic layer ownership and metric governance, and leading multi-disciplinary data teams. What You'll Do: 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. This role may require occasional travel (up to 20%) for team meetings, training, or company events. This is not a complete list of responsibilities, and the scope of the role may evolve with the needs of the team and business. What We're Looking For: Must-have skills or experience: - 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 -