Apple Experience
Building Operational Data Infrastructure at Apple
Worked across analytics, social listening, and data science workflows within AppleCare Digital to build and maintain production-grade data pipelines, anomaly detection systems, and operational reporting infrastructure.
~20
Production Pipelines
100k+
Social Posts / Day
400K+
Items Classified / Day
68%
Pipeline Runtime Cut
Core Systems
Analytics Infrastructure
- ·Built and maintained end-to-end production pipelines
- ·DAGs, cron workflows, aggregate tables & automation
- ·Monitoring, alerts, backfills & operational maintenance
- ·Temporary launch workflows for NPI & WWDC support
- ·Social listening ingestion pipelines from Sprinklr
Tech
Process flow
- Data Ingestion
- Processing
- Automation
- Monitoring
- Operational Tables
KPI Anomaly Detection System
- ·Forecasts each KPI from its own recent history
- ·Scores daily values against expected deviation bands
- ·Distance from the band sets severity — P1, P2, or P3
- ·Anomalies emailed to the stakeholder who owns that KPI
- ·Backed by a centralized monitoring dashboard
Tech
Process flow
- Historical KPI Data
- Prediction Model
- Expected Range Calculation
- Standard Deviation Thresholding
- P1 / P2 / P3 Alerts
- Stakeholder Notifications
Social Listening Classification System
Operational ML workflows for classifying product-related discussions, issues, and feature-level signals from social media and support interactions to improve visibility and proactive response workflows.
- ·Multi-source ingestion workflows
- ·BERT classification pipelines
- ·FLAN-T5 summarization workflows
- ·FAISS vector matching for issue identification
- ·Dynamic issue category creation
- ·GPU-accelerated processing with Airflow orchestration
Tech
Process flow
- Multi-source Data
- Deduplication
- BERT Classification
- FLAN-T5 Summarization
- FAISS Matching
- Issue Categorization
- Dashboard Visibility
My rule at Apple was simple: never do the same task twice. Anything manual I inherited got automated — so an entire analytics org's reporting, alerting, and data workflows kept running without needing someone to babysit them.