Director, Product Analytics - India - Cvent, Inc.

Description

Overview

Reports to: Head of Analytics, Matrixed to Chief Product Officer

Why this role exists Cvent is scaling its product analytics capability to serve a large, multi‐product portfolio ("Attendee Hub", "Registration/Event Management", "OnArrival", "Marketplace/CSN", "Exhibitor Solutions", and "Cvent Essentials"). We need a senior leader to build the operating system for product analytics—from metric contracts and instrumentation to a governed semantic layer and self‐serve insights—so teams can move from question → decision in minutes, not weeks.

What you will own

- Metric Contracts & Semantic Layer: Define and govern product KPIs and their lineage (adoption, activation, engagement, feature usage, time‐to‐value, Events Under Management (EUM), retention) and tie them directly to commercial outcomes (GRR/NRR, expansion, contraction).

- Instrumentation Engineering: Standards, naming/versioning, tracking plans, CI checks, coverage dashboards, and error budgets for data quality (freshness, accuracy, completeness).

- Self‐Serve Insights & Enablement: A scalable, governed self‐serve model (standard dashboards + explores), data literacy curriculum, office hours, and durable documentation.

- Identity & Data Design: User/account identity resolution across web, mobile, onsite devices (e.g., badge printers/kiosks), and partner integrations; deterministic keys and join strategies.

- Analytics Operating Cadence: Monthly decision readouts, portfolio‐level rollups, and "What We Learned" syntheses that change roadmaps and bet sizing.

- Tooling Strategy & TCO: Rationalize and integrate the analytics stack (product analytics, BI/semantic layer, observability, feature flags); drive buy‐vs‐build decisions and vendor governance.

- Team & Org Design: Work closely with leaders / managers who can run Platform & Instrumentation, Decision Science, and Insights & Enablement. Establish clear interfaces with Data Engineering, Security/Privacy, PMM, CS, and UXR.

Note on experiments: While experimentation isn't the primary focus today, you will establish right‐sized guardrails and a playbook (e.g., A/B where feasible, holdouts, basic power/MDE guidance, SRM detection) so the org is future‐ready without over‐rotating now.

In This Role, You Will

- Publish the Cvent Product Metrics Charter (north stars, driver trees, metric definitions, ownership, SLA for freshness) and keep it current.

- Stand up tracking plans and CI checks tied to PRDs; reach high instrumentation coverage for critical flows across products.

- Build a governed semantic layer and standard portfolio dashboards that roll up by product, persona, and account.

- Launch a data literacy program (workshops, office hours, docs) to drive confident self‐serve use by PMs, PMM, UX, CS, and leaders.

- Partner with Data Engineering on data contracts, dbt models, observability, cost management, and access controls; partner with Security/Legal on PII, retention, and privacy‐by‐design.

- Operationalize account‐level analytics (seats/licenses, feature entitlements, health scoring, expansion/contraction funnels) with explicit links to GRR/NRR.

- Produce decision‐quality narratives (not just dashboards): monthly "What we learned," portfolio scorecards, and ad‐hoc deep dives for exec forums.

- Hire, coach, and retain a high‐performing team; set career paths, operating rhythms, and quality bars.

Here's What You Need

Must‐have

- 10–12+ years in product analytics/decision science for enterprise or B2B SaaS; 4+ years leading managers and building multi‐disciplinary teams.

- Proven ownership of metric governance & semantic layers (e.g., LookML/semantic models or equivalent) across multiple products.

- Expert SQL; proficiency with Python for analysis and production‐grade notebooks.

- Demonstrated success establishing instrumentation standards, CI checks, and data quality SLAs (freshness/accuracy/completeness) in partnership with Data Engineering.

- Experience unifying user/account identity across surfaces and offline/onsite data sources.

- Track record driving self‐serve adoption and data literacy at scale (training, playbooks, enablement).

- Experience measuring and operationalizing GenAI/ML systems in production, including defining success metrics, evaluating offline and online performance, supporting experimentation and human‐in‐the‐loop feedback, and translating model behavior into product and business decisions.

- Executive presence and storytelling: turning evidence into clear choices that change roadmaps and investment.

Nice‐to‐have

- Exposure to experimentation at scale (A/B, holdouts, basic variance reduction) and the judgment to right‐size usage.

- Experience mapping product behaviors to commercial metrics (GRR/NRR, expansion/contraction) and account health scoring.

- Familiarity with event‐driven architectures, product telemetry on mobile/edge devices, and privacy‐by‐design.

How we'll measure success

- Instrumentation Coverage: ≥95% of GA features ship with validated tracking plans; minimal schema breakages escaping to prod.

- Reliability SLAs: Data freshness within target windows for core dashboards; accuracy/completeness within agreed error budgets.

- Self‐Serve Adoption & Satisfaction: High monthly active use by PMs in governed explores/dashboards; PM CSAT ≥ target.

- Decision Latency: Significant reduction in time from question → decision in pilot business units.

- Business Linkage: Documented cases where analytics led to changes in roadmap/investment and moved EUM, adoption, or GRR/NRR.

Key focus areas

- Platform & Instrumentation: Tracking plans, CI, observability, coverage dashboards, data contracts.

- Decision Science: Deep dives, driver trees, account health models, right‐sized experimentation playbook.

- Insights & Enablement: Standard dashboards, governed explores, literacy curriculum, office hours, documentation.

How you'll work with partners

- Product Management: Metric definitions, priorities, evidence‐backed decisions.

- Data Engineering: Pipelines, models, contracts, observability, cost; joint SLAs.

- Security/Legal/Privacy: PII handling, retention, consent, governance.

- UX Research: Pair on mixed‐methods insights; Product Analytics focuses on quant, UXR on qual craft and Research Ops.

- PMM/CS/RevOps: Win/loss themes, adoption/usage insights, account health signals that tie to commercial outcomes.

Preferred tools & practices

Product analytics & telemetry (e.g., Mixpanel, Rudderstack, custom event pipelines), BI/semantic layer (e.g., Sigma), data warehouse (e.g., Snowflake), notebooks, observability/quality , feature flags (e.g., LaunchDarkly), documentation hubs, and modern CI/CD.

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