Director of Product Data and Analytics

Responsibilities

You’ll lead Analytics Engineering and Product Analytics at Workato. Your team will help us understand how enterprises adopt AI and orchestration, where promising projects stall, and what changes when customers expand from one successful deployment to many. You’ll work directly with Product leadership, guide the engineers who make usage data reliable, and develop the analysts who turn that data into decisions and original industry research.

What you’ll own

Data strategy and delivery

• Set the strategy, roadmap, priorities, and capacity plan for both teams. Balance investments in shared data foundations with time-sensitive product decisions and investigations the team initiates.
• Define who owns product instrumentation, pipelines, datasets, metrics, and analysis. Agree with Product and Engineering on handoffs and who makes technical and analytical decisions.
• Turn major requests into scoped initiatives with owners, milestones, and success criteria. Resolve dependencies across Product, Engineering, Infrastructure, and Security. Bring progress, risks, and resource trade-offs to leadership early enough to act.

People and organization

• Own hiring, onboarding, performance management, and workforce planning. Identify the skills and leadership capacity the organization needs as Workato’s portfolio grows.
• Set clear expectations and career paths for managers, analytics engineers, and product analysts. Give engineers ownership of architecture and reliability. Coach analysts to frame a question, challenge an assumption, and defend a recommendation with evidence.

Analytics engineering and the data platform

• Lead the engineering work to collect, model, transform, and curate product usage data. Deliver analytics-ready datasets that connect builder activity, workspaces, customer accounts, deployments, and production workloads.
• Partner with senior engineers on architecture, orchestration, and tooling. Weigh scalability, maintainability, cost, and delivery speed when deciding whether to extend a data model, build a pipeline, or change the platform.
• Establish standards for automated testing, CI/CD, documentation, lineage, and pipeline monitoring. Give critical datasets an owner and a way to detect stale, missing, or incorrect data before it affects a decision.

Product analytics and experimentation

• Bring analysts into roadmap discussions while options are open. Understand the choices a product leader faces, test competing explanations, and identify what evidence would change the recommendation.
• Define measurement during feature planning. Agree with PMs and engineers on the intended customer outcome, what telemetry to capture, who will implement it, and how to validate it before launch. Review the results after release.
• Measure activation, engagement, retention, and expansion. Show whether growth comes from more customers reaching production, more business processes per customer, or greater volume in existing workloads. Each finding points to a different product decision.
• Lead experiment design and interpretation. Define hypotheses, success metrics, and guardrails. Use funnels, cohorts, A/B tests, and holdouts where appropriate. Explain what the team can conclude when enterprise sample sizes are small.
• Match the analysis to the stage of the product. Identify early signs of customer value for new offerings. Forecast adoption and growth for established products. Recommend where to invest, what to change, and when to measure again.

Metrics, self-service, and AI-assisted analytics

• Publish a shared catalog of product and business metrics. Document each metric’s definition, calculation, source, and owner. Verify that the data platform, dashboards, and reports implement it consistently.
• Give analysts and business teams governed datasets and reusable tools for reporting, experimentation, and advanced analysis. Make recurring questions easy to answer without a new engineering request.
• Develop AI-assisted analytics that can monitor metrics, investigate changes, and explain findings. Ensure answers respect access permissions and let users verify the source and calculation.

Data culture and industry research

• Establish regular reviews where Product, Sales, and Customer Success discuss a usage pattern, decide what to do, and return to see whether it worked. Help field teams use those findings to address adoption barriers and identify expansion opportunities.
• Lead original research for the Workato Automation Index and industry briefings. Use aggregated usage data to examine which processes enterprises automate, who builds them, and where AI is moving into production. Test emerging trends against customer and market evidence, then partner with Marketing and Analyst Relations to publish findings the industry can use.

Requirements

What you bring

• 10+ years in analytics, data science, or a related quantitative field, including experience leading senior practitioners, developing managers, and scaling teams.
• Data engineering and modeling expertise. You can review SQL and architecture decisions and guide teams working with cloud warehouses, transformation frameworks, and orchestration tools such as Snowflake and dbt.
• Data cataloging and governance experience. You’ve used Atlan or comparable tools to document ownership and lineage, govern access, and make trusted datasets discoverable.
• B2B product and analytical judgment. You know how to use behavioral analysis, forecasting, experimentation, and causal inference to guide a decision. You can explain a decision your team changed and what happened afterward.
• Cross-functional leadership. You’ve delivered work where teams share ownership of data and infrastructure. You can resolve dependencies, negotiate priorities, and explain trade-offs to engineers and executives.
• Practical AI fluency. You use AI to investigate questions and verify findings. Experience building analytics agents or governed self-service data products is a plus.

What success looks like

• Priority launches ship with validated telemetry. In post-launch reviews, PMs can show who reached the intended outcome, where adoption stalled, and what they will change.
• Product and field teams can answer recurring questions from shared datasets. They spend less time reconciling definitions or waiting for custom queries.
• Engineers own reliable data assets. Analysts spend more time investigating behavior and testing hypotheses. Both teams have clear priorities, career paths, and a leader who resolves cross-functional trade-offs.
• Product leaders seek the team’s perspective before committing to investments. Sales and Customer Success use its findings in customer conversations. Workato publishes original research that helps customers, partners, and analysts understand how enterprise AI and automation are evolving.

(REQ ID: 2538)