Director of Product Data and Analytics

Workato•Palo Alto, CA

About The Position

The Director of Product Data and Analytics will lead Workato’s Analytics Engineering and Product Analytics teams. The role helps the company understand how enterprises adopt AI and orchestration, where initiatives stall, and how customer usage changes as deployments expand. The director works directly with Product leadership, leads the engineers responsible for reliable usage data, and develops analysts who turn data into decisions and industry research. The director sets strategy, roadmaps, priorities, and capacity plans across both teams; defines ownership and handoffs for instrumentation, pipelines, datasets, metrics, and analysis; and turns major requests into scoped initiatives with clear owners, milestones, and success criteria. The role also owns hiring, onboarding, performance management, and workforce planning, while building career paths and leadership capacity. The position leads the collection, modeling, transformation, and curation of product usage data, delivering analytics-ready datasets that connect builder activity, workspaces, customer accounts, deployments, and production workloads. It partners with senior engineers on architecture, orchestration, and tooling, and establishes standards for testing, CI/CD, documentation, lineage, and monitoring. The director brings analysts into roadmap discussions, defines measurement with product managers and engineers during feature planning, and reviews outcomes after releases. The role guides measurement of activation, engagement, retention, and expansion; leads experimentation and interpretation; publishes shared metric definitions; and enables governed self-service and AI-assisted analytics. It also establishes data-informed review practices across Product, Sales, and Customer Success, and leads original research for the Workato Automation Index and industry briefings.

Requirements

10+ years of experience in analytics, data science, or a related quantitative field.
Experience leading senior practitioners, developing managers, and scaling teams.
Data engineering and modeling expertise, including the ability to review SQL and architecture decisions and guide teams using cloud warehouses, transformation frameworks, and orchestration tools such as Snowflake and dbt.
Experience with data cataloging and governance, using Atlan or comparable tools to document ownership and lineage, govern access, and make trusted datasets discoverable.
B2B product and analytical judgment, including behavioral analysis, forecasting, experimentation, and causal inference to guide decisions.
Ability to explain a decision changed by the team and what happened afterward.
Cross-functional leadership experience delivering work across teams that share ownership of data and infrastructure.
Ability to resolve dependencies, negotiate priorities, and explain trade-offs to engineers and executives.
Practical AI fluency, including using AI to investigate questions and verify findings.

Nice To Haves

Experience building analytics agents or governed self-service data products.

Responsibilities

Set strategy, roadmaps, priorities, and capacity plans for Analytics Engineering and Product Analytics.
Balance shared data-platform investments with time-sensitive product decisions and investigations.
Define ownership of product instrumentation, pipelines, datasets, metrics, and analysis, and agree on handoffs and decision-making with Product and Engineering.
Scope major requests into initiatives with owners, milestones, and success criteria; resolve cross-functional dependencies and communicate progress, risks, and resource trade-offs to leadership.
Own hiring, onboarding, performance management, and workforce planning for the teams.
Establish expectations and career paths for managers, analytics engineers, and product analysts; coach team members and build leadership capacity.
Lead the collection, modeling, transformation, and curation of product usage data and deliver analytics-ready datasets.
Partner with senior engineers on data architecture, orchestration, and tooling, weighing scalability, maintainability, cost, and delivery speed.
Establish standards for automated testing, CI/CD, documentation, lineage, and pipeline monitoring; ensure critical datasets have owners and data-quality monitoring.
Bring analysts into roadmap discussions and use evidence to evaluate product choices and competing explanations.
Define feature measurement with product managers and engineers, including intended outcomes, telemetry, implementation ownership, and validation; review results after release.
Measure activation, engagement, retention, and expansion, distinguishing among customer adoption, breadth of processes, and workload volume.
Lead experiment design and interpretation, including hypotheses, success metrics, guardrails, funnels, cohorts, A/B tests, and holdouts where appropriate.
Match analyses to product maturity, identify early customer-value signals, forecast adoption and growth, and recommend investments, changes, and follow-up measurement.
Publish and maintain a shared catalog of product and business metrics, documenting definitions, calculations, sources, and owners and ensuring consistent implementation.
Provide analysts and business teams with governed datasets and reusable tools for reporting, experimentation, and advanced analysis.
Develop AI-assisted analytics that monitor metrics, investigate changes, and explain findings while respecting permissions and enabling verification of sources and calculations.
Establish regular reviews where Product, Sales, and Customer Success discuss usage patterns, choose actions, and evaluate whether those actions worked.
Help field teams apply findings to adoption barriers and expansion opportunities.
Lead original research for the Workato Automation Index and industry briefings using aggregated usage data, and partner with Marketing and Analyst Relations to publish findings.