My Role

Product Leader, Amazon QuickSight
I Partnered across Product, Engineering, Design, and AWS Leadership (including the CEO) to define QuickSight’s vision, shape the roadmap, and guide its evolution into AWS’s flagship BI service.

• Strategy & Roadmap: Collaborated with PMs, engineering, and leadership to refine long-term product strategy, prioritize features, and align development with target customer segments.

• Pricing & Packaging: Helped define usage-based pricing and packaging to expand reach and drive adoption.

• Operational Planning: Participated in org-level planning and business reviews to ensure product objectives tied to company-wide targets.

• Go-to-Market: Developed GTM strategy connecting product capabilities to customer value propositions and competitive positioning.

• Product Marketing: Led messaging, content strategy, and customer education programs to accelerate awareness and onboarding.

• Sales Enablement: Built tools, collateral, and training to align sales execution.

The Setup

QuickSight was AWS’s push to build a modern, cloud-native BI platform in a market dominated by mature incumbents like Tableau, Power BI, and Looker, as well as the legacy BI vendors (SAP, Oracle, IBM, Qlik).

• Defining Focus – Instead of chasing feature parity across the entire BI landscape, we defined a clear focus around use cases where AWS had a natural advantage: scale, cost efficiency, and deep data integrations with services like Redshift, S3, and Athena.

• Customer Alignment – This strategy targeted large enterprise customers, and developers already embedded in AWS, aligning the early roadmap around delivery analytics at scale.

• Early Product-Market Fit – That focus allowed QuickSight to establish early traction, reduce friction from feature gaps, and create a differentiated foothold the team could expand from as the product matured.

The Work

With the product focus defined, our next phase was execution, rapidly evolving QuickSight’s capabilities, scaling adoption, and establishing its position as AWS’s native analytics solution.

Initial Launch: Built the complete launch motion including website, demos, tutorials, webinars, and re:Invent sessions, while delivering critical feature improvements.

Roadmap:

• Analytic Capabilities: 10 new visualizations, advanced filtering, alerting, pixel-perfect reporting, and AI analytics features (natural language queries, automated insights, predictive analytics, anomaly detection) that later became known as Q.

• Deployment: User management, role-based security, Active Directory integration, row/column-level data security, and embedding SDK.

• SPICE / Data Integrations: 15 new data connectors (including Snowflake, Athena, Teradata, Databricks, BigQuery, and Adobe Analytics); increased SPICE capacity to 1B rows or 1TB per dataset.

Go-to-Market: Created all marketing and enablement assets (website, blogs, educational content, campaigns, onboarding, sales training, webinar program).

Internal Adoption: Worked with teams from across all of AWS and Amazon retail to replace legacy BI tools with QuickSight, generating millions in cost savings, critical customer feedback (which helped shape roadmap), and case studies for external customers.

Pricing Innovation: Introduced the BI industry’s first usage-based pricing model (charging per-user for authors and per-session for viewers) dramatically reducing cost barriers for large-scale deployments.

Sales Enablement: Built the business case and implementation plan for a new overlay sales team dedicated to QuickSight, which significantly accelerated adoption and revenue growth. Also developed all sales collateral and sales training programs for AWS account managers.

The Results

• Adoption: Generated account +50k signups at launch; later became widely used across AWS’s enterprise and developer customer base.

• Internal Success: QuickSight was deployed across Amazon, replacing legacy BI tools and generating millions in savings.

• Revenue Growth: The combination of usage-based pricing and a dedicated sales overlay team unlocked scalable customer expansion, driving +100% YoY growth each year during my tenure and beyond.

• Differentiation: Embedding capabilities, elastic pricing, and ML-powered analytics (Amazon Q) positioned QuickSight as a unique player in the BI market.

• Organizational Impact: Established GTM discipline and created a new sales motion (specialized sales teams) within AWS, setting the product on a sustainable long-term trajectory.

Looking Back

Joining Amazon was an opportunity to experience product building at global scale. QuickSight was the first product I inherited rather than created, and my mandate was to guide it toward product-market fit and sustainable growth within one of the most complex organizations in the world. As a new service in the expansive AWS portfolio, QuickSight had to earn its place, requiring our team to operate like entrepreneurs inside a global enterprise, moving fast, thinking creatively, and proving value through results.

The experience was a masterclass in collaboration and leadership. I worked not only across engineering, design, and product within QuickSight but also with dozens of other AWS services (data, compute, security, and AI/ML) to power our capabilities. Beyond that, I partnered with global sales, marketing, and solutions architect teams to build GTM and customer success programs, while leading an internal adoption initiative that connected me with teams across nearly every part of Amazon.

Regular interaction with AWS’s senior leadership provided a firsthand education in how one of the world’s most effective organizations achieves alignment, scale, and operational precision. The experience fundamentally shaped how I think about influence, collaboration, and driving innovation inside large, interconnected systems.

QuickSight 2016 Pre-Launch