Joseph Bruechner

Analytics Engineer & Data Infrastructure Specialist

Building scalable data infrastructure and self-service analytics platforms that empower teams to make data-driven decisions. Specialized in BigQuery data warehousing, business intelligence, and translating complex usage data into actionable insights for AI and SaaS companies.

2.5+
Years Experience
85%
Reporting Time Reduction
40%
Model Evaluation Efficiency
99.8%
Data Accuracy Achieved

Technical Expertise

Analytics Infrastructure

  • BigQuery, PostgreSQL, ChromaDB
  • ETL/ELT Pipeline Design
  • Data Warehouse Architecture
  • Data Quality & Monitoring
  • Self-Service Analytics Platforms

Business Intelligence

  • Looker, Tableau, Power BI
  • Dashboard Development
  • KPI Tracking & Metrics
  • A/B Testing Frameworks
  • Customer Analytics & Segmentation

Development & Cloud

  • Python (Pandas, SQLAlchemy, FastAPI)
  • SQL (Advanced Queries & Optimization)
  • AWS (S3, Lambda, SageMaker, QuickSight)
  • GCP (BigQuery, Cloud Functions)
  • Docker, Git, CI/CD

Domain Expertise

  • SaaS Metrics (MRR, ARR, Churn, LTV)
  • Product Analytics & Usage Tracking
  • Revenue Operations Analytics
  • AI/ML Product Performance
  • Cross-Functional Collaboration

Featured Projects

SaaS Financial Analytics Pipeline

Data Infrastructure & Business Intelligence

Built comprehensive data warehouse for SaaS business model with automated ETL processes, dimensional modeling, and self-service analytics. Implemented MRR/ARR calculations, customer segmentation, and retention analysis using BigQuery ML for anomaly detection.

BigQuery Looker Python SQL dbt-style
Business Impact

Reduced month-end reporting from 5 days to 2 hours (96% improvement) • Enabled finance team self-service analytics • Automated data quality monitoring achieving 99.8% accuracy

Real-Time Market Analytics Platform

Interactive Dashboard & Data Engineering

Developed interactive analytics platform for real-time financial market analysis with ETL pipeline, technical indicators, and comparative analysis. Features intelligent data caching, fallback systems, and responsive design for executive reporting.

Python Dash Plotly API Integration Real-time Processing
Technical Achievement

99% uptime with intelligent fallback systems • Real-time data processing with sub-second response times • Interactive multi-tab interface supporting complex analysis workflows

Legal AI Platform Analytics

Product Analytics & Multi-Tenant Architecture

Designed analytics infrastructure for AI-powered legal research platform with firm-specific data isolation, RAG system performance monitoring, and conversational AI usage analytics. Built HIPAA-compliant audit logging and subscription tier analysis.

FastAPI PostgreSQL ChromaDB Docker Multi-tenant
Platform Metrics

Multi-tenant architecture supporting 100+ law firms • Real-time usage analytics for AI model optimization • Compliance-ready audit logging and data governance

Analytics Infrastructure Framework

Evaluation Platform & A/B Testing

Built modular framework for evaluating analytics models and A/B testing workflows. Enables standardized model comparison across product teams with automated reporting, statistical significance validation, and performance monitoring dashboards.

Python Scikit-learn Statistical Analysis Automated Reporting
Efficiency Gains

40% reduction in model evaluation time • Standardized evaluation across product teams • Automated A/B test significance calculations and reporting

Healthcare Operations Analytics

Predictive Analytics & Resource Optimization

Developed predictive analytics solution for hospital length of stay optimization, combining patient data analysis with operational insights. Created actionable recommendations for department resource allocation and cost reduction strategies.

Python Pandas AWS Deployment Healthcare Analytics
Operational Impact

10% reduction in average patient stay • $2,000 cost savings per optimized case • Real-time deployment enabling proactive resource allocation