OLAP Analytics Platform

Championed a company-wide initiative to decouple analytics from production infrastructure, reducing data processing time by 30% and unblocking app scalability across a multi-tenant SaaS platform.

OLAP Analytics Platform

About the Project

As the company scaled, analytics was running directly on the production database — a pattern that worked at startup scale but created two compounding problems. The production system was absorbing growing analytics load, degrading performance. More critically, the Analytics team's data requirements were driving backend architecture decisions for the Software Engineering team — forcing them to design storage around analytics query patterns instead of optimal user experience. In a multi-tenant environment, this coupling was unsustainable. The solution was clear: a dedicated OLAP analytics platform with ETL pipelines to fully decouple the two systems and unblock both teams.

My Role

I championed and owned end-to-end delivery — from stakeholder alignment and requirements engineering through phased execution and go-live. I coordinated across Analytics, Data Engineering, Software Architecture, and Product teams, built the Analytics team's roadmap from scratch (a prerequisite for designing a scalable solution), and maintained tri-directional communication across leadership, delivery teams, and cross-functional stakeholders throughout.

Program Management Approach

  • Discovery & Alignment
    • Partnered with the VP of Analytics and SW Architect to map pain points and define success criteria for each team
    • Identified that the Analytics team had no roadmap — built one with them to ensure the architecture would scale beyond immediate needs, not just solve today's problem
  • Requirements Engineering
    • Ran multiple requirements-gathering sessions covering CRM data, user transactions, demographic data, session information, and refresh cadence — capturing both current and future needs
    • Prioritized requirements by business impact; collaborated with the Data Architect on t-shirt estimates for each workstream
  • Charter & Governance
    • Authored the project charter: scope, will-do/won't-do, risks, and phased delivery plan
    • Ran kick-off with Data Engineering and Analytics; received sign-off from all stakeholders before execution began
  • Phased Execution
    • Created Jira Epics mapped to deliverables, dependencies, and project phases; managed sprint planning for the Data Engineering team
    • Mapped cross-phase dependencies and blockers; coordinated with the Product team to sequence work around other in-flight features
    • Weekly syncs to unblock the team; weekly status updates to leadership — upward, downward, and sideways communication throughout
  • Transition & Adoption
    • Led the Analytics team through a structured migration plan — moved Tableau dashboards and analytics workflows to the new analytics DBs without disrupting live reporting
    • Rebuilt and migrated database views to point at the analytics layer, and optimized cross-table joins for analytics query patterns — replacing ad-hoc production joins with purpose-built, performant structures

Tech Details

Analytics Database (OLAP)
Dedicated OLAP database with an analytics-optimized schema — fully separated from production, designed for the aggregation-heavy query patterns and refresh cadences the Analytics team actually needed rather than normalized OLTP constraints.
ETL Pipelines
Automated pipelines ingesting CRM, transaction, demographic, and session data from production on defined refresh cadences — decoupling analytics read load entirely and giving the team control over when and how data is surfaced.
Views & Query Optimization
Built and migrated purpose-built database views on the analytics layer, replacing production ad-hoc queries. Optimized cross-table joins for analytics query patterns — eliminating slow, fragile joins that were designed for transactional writes, not analytical reads.
Multi-Tenant Architecture
Schema and pipeline design enforced tenant-level data isolation and cross-tenant performance boundaries — a non-negotiable constraint that shaped every data model and access control decision.
BI & Reporting Migration
Tableau dashboards and analytics workflows migrated from production-backed queries to the new analytics layer — analysts could now run complex, long-running queries with no risk of impacting production performance or locking tables.
Platform Capabilities Unlocked
The dedicated layer provided the structured foundation for descriptive, diagnostic, and prescriptive analytics — and established the clean, well-modeled data baseline required for future predictive modeling and ML work.

Outcomes

30%
Reduction in data processing time
100%
Analytics load removed from production DB
3 tiers
Descriptive, diagnostic & prescriptive analytics enabled
On time
All project phases delivered on schedule
Unblocked
SW teams freed from analytics-driven design constraints
ML-ready
Roadmap created for future ML capabilities

Strategic Impact:

  • Production system protected at scale — analytics load eliminated as a production bottleneck, with headroom for continued company growth.
  • SW Engineering unblocked — teams no longer designing storage around analytics needs; free to optimize for functionality and user experience.
  • Analytics team elevated — moved from reactive, constrained reporting to a platform capable of descriptive, diagnostic, and prescriptive analysis, with the clean data foundation needed to pursue predictive modeling.
  • The platform is now the foundation for ML capabilities, with a roadmap in place to build on the groundwork laid in the initial phases.