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Quip (Salesforce)

Data Platform Reliability & Analytics

Impact Summary

Increased overall system uptime by 15% and enabled real-time insights via a new analytics pipeline, while reducing operational costs by 20% through ECS migration.

Role

Technical Lead – Data Platform SRE

Timeline

2020–2021

Scale

  • High Availability
  • Real-Time Data
  • 15% Uptime Increase

Links

Internal / Confidential

Problem

The Quip Data Platform was facing reliability issues that impacted the availability of critical business insights. Additionally, the existing analytics infrastructure was batch-based, preventing real-time analysis of user interactions. The lack of real-time visibility meant product teams were flying blind during feature launches, and operational instability was causing excessive on-call burden.

Approach

As the Technical Lead for Data Platform SRE, I focused on two main areas: hardening the existing infrastructure and modernizing the data pipeline.

Reliability Engineering

I led a comprehensive reliability audit of the platform, identifying single points of failure and bottlenecks. We implemented better monitoring and alerting, and automated recovery processes.

  • Uptime: These efforts resulted in a measurable 15% increase in overall system uptime.
  • Automation: We automated the provisioning of all new infrastructure using Terraform, eliminating manual configuration drift.

Real-Time Analytics

To enable real-time insights, I designed and built a new clickstream analytics pipeline.

  • Architecture: We used AWS Kinesis for data ingestion, Lambda for serverless processing, and Athena for ad-hoc querying.
  • Impact: This allowed product teams to see user behavior in real-time, enabling faster iteration.

Outcomes

  • Stability: The platform became significantly more stable, reducing on-call burden and improving trust with stakeholders.
  • Visibility: The business gained new capabilities to understand user engagement as it happened.
  • Efficiency: The move to ECS and serverless technologies lowered our run rate.
  • Cost Savings: Migrated legacy applications to AWS ECS, which reduced operational overhead and costs by 20%.

Key Contributions

  • Pipeline Development: Architected and implemented the core logic for the Kinesis/Lambda pipeline.
  • Cost Optimization: Led the migration of legacy services to AWS ECS, optimizing resource utilization.
  • Infrastructure as Code: Standardized Terraform usage across the data platform team.
  • Reliability Leadership: Mentored the team on SRE best practices, shifting culture from reactive to proactive.