The Challenge
A unicorn startup ($1B+ valuation) needed their internal stakeholders — product managers, operations leads, executives — to build dashboards and extract insights from raw data. The problem: the existing approach was expensive, slow, and bottlenecked on a small data team.
Every insight request followed the same painful loop:
- Stakeholder submits a request to the data team — "Show me X broken down by Y for the last quarter"
- Data team writes SQL, builds the visualization, sends it back days later
- Stakeholder wants a tweak — "Actually, can you filter by region too?" — and the cycle restarts
- Cost was unsustainable — the tooling and team overhead for analytics was disproportionate to the value delivered
They needed an Apache Superset-style platform purpose-built for their data infrastructure — one where non-technical users could explore data, build dashboards, and get answers without writing SQL or waiting on the data team.
The Solution
Technical lead on an enterprise-grade analytics platform built from the ground up. Ongoing engagement since May 2025.
LLM-Powered Query Optimization
The core differentiator: stakeholders describe what they want in plain English, and the system builds the query.
- LLM APIs translate natural language into optimized SQL queries against the organization's actual schema
- Query validation layer ensures generated SQL is safe, performant, and scoped to the user's permissions
- Non-technical users get the same quality of analysis that previously required a data engineer
ETL Pipeline Architecture
- Processing 100M+ data points through extraction, transformation, and loading pipelines
- Multi-database connectivity — pulling from multiple source databases across the organization
- On-demand sync keeping dashboards current without the overhead of continuous replication
- Incremental processing — only new and changed data flows through the pipeline, not full refreshes
Performance Engineering
- Lazy async caching for visualization data — dashboards load from cache while fresh data computes in the background
- P95 visualization latency <200ms — fast enough that exploring data feels interactive, not like waiting for a report
- Redis-backed caching layer with intelligent invalidation tied to data freshness signals
Dashboard & Visualization
- Self-service dashboard builder for stakeholders — drag-and-drop chart creation, filter controls, date range selectors
- Real-time insights delivery — dashboards update as new data flows through the ETL pipeline
- Export and sharing capabilities for executive reporting
Tech Stack
- Frontend: React, Tailwind CSS
- Backend: Python, FastAPI, Celery, Java, Spring Boot
- Data: Apache Pinot, Kafka, PostgreSQL, Redis
- Infrastructure: AWS, Docker, Jenkins
The Impact
- 100M+ data points processed through ETL pipelines with real-time insights delivery
- 90% cost reduction vs. the previous analytics approach — eliminating expensive tooling and reducing data team bottleneck
- P95 <200ms visualization latency — dashboards feel instant
- Non-technical users empowered to build their own dashboards and queries via LLM-powered interface
- Data team unblocked — freed from ad-hoc query requests to focus on strategic data work
This engagement is ongoing. Metrics will be updated as the platform scales.
Testimonial
Testimonial pending — engagement in progress.