Case Study

Real-Time Event Intelligence Platform

Fortune 100 streaming data platform processing 10B+ events annually

Kafka AWS Python dbt Kinesis Lambda Data Engineering Streaming

SHIPPED · ENTERPRISE

As part of a platform team at a Fortune 100 company, I designed and built a high-volume event processing platform that ingests, transforms, and serves billions of data points annually — powering real-time AI-driven decisioning, personalized customer messaging, campaign analytics, and ML feature pipelines across the organization.

The Problem

No unified platform. Fragile pipelines. Zero observability.

When I joined the platform team at a Fortune 100 company, the organization was processing billions of transactional, lifecycle, engagement, and behavioral events annually — but there was no unified platform to ingest, normalize, and serve this data. Engineering teams were building one-off pipelines, duplicating ingestion logic, and maintaining fragile point-to-point integrations.

Every new use case meant building a new pipeline from scratch. There was no shared transformation layer, no consistent schema strategy, and no way for downstream teams to self-serve. Data freshness was inconsistent — some pipelines delivered in minutes, others took hours. And when things broke, there was no observability. Teams were finding out about data incidents from stakeholders, not from monitoring.

The cost wasn't just engineering time — it was business impact. Campaigns launched on stale data. ML and AI decisioning systems trained or reasoned on inconsistent features. Analytics teams spent more time debugging data quality than generating insights.

What I Built

End-to-end streaming platform, from ingestion to self-service.

Transaction Events

User Lifecycle

Engagement

Behavioral Signals

↓

Kafka / Kinesis — Streaming Ingestion

↓

AWS Lambda

Python Processors

Schema validation · Deduplication · Normalization

↓

Bronze — Raw

→

Silver — Normalized

→

Gold — Analytics-Ready

250+ dbt SQL models

↓

AI Decisioning & Messaging

ML Feature Store

Campaign Analytics

Built Python-based AWS Lambda services to ingest billions of events from upstream systems via Kinesis, transforming raw streams into normalized datasets
Engineered layered transformation architecture — new use cases reuse existing streams without duplicating ingestion logic
Delivered dbt-modeled medallion architecture with 250+ SQL models and automated data quality checks
Built an internal data portal enabling engineering teams to discover, query, and manage real-time data streams
Contributed to AI-driven decisioning — using RAG and LLMs to personalize customer messaging based on real-time user context, events, and preferences
Implemented comprehensive observability using CloudWatch, structured logging, and custom data quality metrics

Key Architecture Decisions

Four decisions that shaped the platform.

Layered Transformation Over Point-to-Point

Instead of building a new pipeline for every use case, I designed a composable architecture where normalized facets feed into derived facets and moments. New use cases plug into existing streams. This eliminated 60%+ of duplicated ingestion logic.

Medallion Architecture with dbt

Bronze for raw ingestion, Silver for normalized transforms, Gold for analytics-ready datasets. Every layer has automated quality checks — uniqueness, referential integrity, freshness. 250+ models, all tested, all documented.

AI Decisioning with RAG

The platform doesn't just move data — it powers decisioning. I helped build AI-driven messaging flows using RAG to retrieve real-time user context, and LLMs to personalize content, tone, and channel selection. The shift from rule-based to intelligent, context-aware decisioning.

Observability-First Design

I built the monitoring before the pipeline. CloudWatch dashboards, structured logging, custom data quality metrics. When something breaks, we know before stakeholders do. MTTR dropped 40%.

Results

The platform became the central nervous system for real-time data.

10B+

Events processed annually

< 5 min

End-to-end latency SLA

250+

dbt models in production

40%

Reduction in MTTR

The platform became the central nervous system for real-time data across the organization. Engineering teams went from building bespoke pipelines to self-serving through the data portal. Campaigns launched on data that was minutes old instead of hours. ML and AI teams got consistent, tested features to power intelligent decisioning and personalized customer messaging. And when something broke, we knew about it before anyone else did.

Tech Stack

Python AWS Lambda Kinesis Kafka dbt Snowflake LLMs RAG Docker Kubernetes Helm CloudWatch Delta Lake