Fractional CTO for AdTech and MarTech platforms

Building scalable advertising and marketing technologies with proven expertise in recommendation systems, bid optimization, attribution modeling, and high-performance data pipelines.

Challenge

AdTech/MarTech challenges

Scale and performance: handling millions of requests per second with <100ms latency
Data pipeline complexity: processing, storing, and analyzing billions of events
Real-time decisions: bid optimization, ad selection, audience targeting in real-time
Attribution modeling: multi-touch attribution across channels and devices
Privacy and compliance: GDPR, CCPA, cookie deprecation, privacy sandbox
Integration hell: connecting to dozens of ad networks, DSP, SSP, DMP

Cost optimization: cloud infrastructure costs grow with scale
Fraud detection: bot traffic, click fraud, impression fraud
Scale and performance: handling millions of requests per second with <100ms latency
Data pipeline complexity: processing, storing, and analyzing billions of events
Real-time decisions: bid optimization, ad selection, audience targeting in real-time

Attribution modeling: multi-touch attribution across channels and devices
Privacy and compliance: GDPR, CCPA, cookie deprecation, privacy sandbox
Integration hell: connecting to dozens of ad networks, DSP, SSP, DMP
Cost optimization: cloud infrastructure costs grow with scale
Fraud detection: bot traffic, click fraud, impression fraud

How I help

High-performance architecture

Systems to handle millions of requests with minimal latency

RTB systems: Bid response <50ms with optimal pricing
Recommendation engines: Personalized content/product recommendations at scale
Event stream processing: Kafka, Kinesis, or Pulsar for real-time data pipelines
Caching strategies: Multi-level caching (Redis, CDN, edge) for performance
Database optimization: Time-series databases, columnar stores for analytics
Scalable infrastructure: Auto-scaling, load balancing, multi-region deployment

Data and analytics

Reliable data processing and analysis for decision-making

Data warehouse architecture: Optimized Snowflake, BigQuery, Redshift
ETL/ELT pipelines: Reliable data loading and transformation
Real-time analytics: Streaming analytics dashboards
Attribution modeling: Multi-touch attribution algorithms and implementation
Data lake design: Cost-effective storage of historical data
ML pipeline infrastructure: Feature stores, model serving, A/B testing

Optimization and intelligence

ML-powered optimization to maximize ROI

Bid optimization algorithms: Maximize ROI with intelligent bidding strategies
Audience segmentation: ML clustering and user targeting
A/B testing frameworks: Multi-armed bandits, Bayesian optimization
Conversion prediction: ML models for lead scoring, churn prediction
Budget allocation: Optimize spend across channels and campaigns
Creative optimization: Dynamic creative optimization (DCO)

Privacy and compliance

Privacy-first solutions for regulatory compliance

GDPR/CCPA implementation: Consent management, data deletion, portability
Privacy-first architecture: First-party data strategies, contextual targeting
Cookie alternatives: Preparing for cookieless future
Data governance: Access control, audit trails, data lineage
Security: Encryption at rest and in transit, secure API design
Compliance automation: Automated reporting and compliance monitoring

Integration and interoperability

Reliable integrations with the AdTech/MarTech ecosystem

API gateway design: Rate limits, authentication, versioning
Webhook reliability: Retry logic, idempotency, monitoring
Third-party integrations: Ad networks, analytics platforms, CRM
Data exchange standards: OpenRTB, Ads.txt, Sellers.json
SDK development: Client libraries for easy integration
Partner onboarding: Self-service portals for integrations

Cost and performance optimization

Reducing costs while maintaining performance

Cloud cost management: Reserved instances, spot instances, right-sizing
Query optimization: Reducing BigQuery/Snowflake costs by 50-80%
Infrastructure efficiency: Container orchestration, serverless where appropriate
Data retention policies: Archiving old data, reducing storage costs
Monitoring and alerts: Prometheus, Grafana, DataDog for cost tracking
Performance profiling: Identifying and eliminating bottlenecks

Common use cases

How I help different types of AdTech/MarTech platforms

Demand-Side Platforms (DSP)

Real-time bidding (RTB) engine
Campaign management and optimization
Audience targeting and segmentation
Conversion tracking and attribution
Budget allocation and pacing

Supply-Side Platforms (SSP)

Header bidding implementation
Yield optimization
Ad fraud detection
Inventory forecasting
Publisher analytics

Marketing analytics platforms

Multi-touch attribution modeling
Customer journey analytics
Marketing mix modeling (MMM)
Predictive analytics and forecasting
ROI reporting and dashboards

Customer Data Platforms (CDP)

User identity and data unification
Unified customer profiles
Audience segmentation
Real-time personalization
Data activation across channels

Ad networks and exchanges

Ad serving infrastructure
Real-time decisioning
Fraud detection and prevention
Reporting and analytics
Publisher and advertiser portals

Technology stack and architecture

Typical technologies and patterns for AdTech/MarTech

Backend

Node.js, Python, Go, Java (for high-performance services)
Kafka, Kinesis, Flink, Spark Streaming
PostgreSQL, MongoDB, Redis, Elasticsearch

Analytics

BigQuery, Snowflake, Redshift, ClickHouse
dbt for data transformation
Looker, Tableau for visualization

ML/AI

TensorFlow, PyTorch, scikit-learn, XGBoost
Feature stores (Feast, Tecton)
MLflow for experiment tracking

Infrastructure

AWS, GCP, Kubernetes, Terraform
Edge computing (Cloudflare Workers, Lambda@Edge)
CI/CD (GitHub Actions, GitLab CI)

Monitoring

Prometheus, Grafana, DataDog, New Relic
ELK stack for logging
Sentry for error tracking

How it works

Month 1: Discovery and architecture

1 month

Audit current infrastructure and performance. Identify bottlenecks and optimization opportunities. Design target architecture. Define key metrics and SLA. Quick wins implementation.

Month 2-3: Core infrastructure

2 months

Implement/optimize data pipeline. Set up monitoring and alerting. Improve caching and database performance. Start ML model development (if applicable). Standardize integrations.

Month 4-6: Optimization and scaling

3 months

Implement A/B testing framework. Advanced optimization algorithms. Cost reduction initiatives. Team training and documentation. Compliance and security hardening.

Results you can expect

Performance improvements

3-5x faster query execution
50-80% reduction in cloud costs
99.95%+ system uptime
Sub-100ms latency for real-time operations
2-3x improvement in data processing throughput

Business impact

20-40% improvement in campaign ROI
30-50% reduction in infrastructure costs
Faster time-to-market for new features
Better data quality and reliability
Improved customer satisfaction (faster dashboards)

Technical maturity

Real-time monitoring and alerting
Automated scaling and deployment
Comprehensive testing and CI/CD
Clear documentation
Data governance and regulatory compliance

Who this is for

Pre-Seed/Seed stage

Building first version of ad/marketing platform. Need scalable architecture from day one. Choosing tech stack for AdTech workloads.

Series A growth

Scaling from thousands to millions of events. Performance optimization is critical. Building data science/ML capabilities.

Series A+ and scaling

Multi-region expansion. Advanced ML and optimization. Building platform for enterprise clients.

Enterprise and scalable platforms

Complex performance and compliance requirements. Need expertise in high-load systems.

Pricing

Suitable packages for AdTech/MarTech platforms

Currency:

Growth CTO

Scale CTO

$11,000per month

~20 hours per week of dedicated time

For Series A+ startups with 15-20+ engineers preparing for the next funding round.

How we work:

Everything from Growth CTO package
Daily involvement in engineering operations - standups, planning, escalations
Executive team participation - board prep, investor meetings, due diligence support
Engineering management development - coaching team leads into engineering managers
Organizational design - team topology, processes, rituals, communication structures
Vendor strategy - cloud cost optimization, service negotiations, contractor management
Technical debt strategy - refactoring roadmap balanced against product delivery

What you walk away with:

Engineering handbook - culture, standards, processes, expectations
Leveling and compensation framework - career ladders and salary bands
Engineering metrics dashboard - DORA metrics, velocity trends, code quality
Incident management playbook - on-call rotation, severity definitions, postmortem process
Technical due diligence package - investor-ready architecture and security documentation
Security and compliance assessment - SOC2, GDPR, HIPAA readiness evaluation
Engineering headcount plan and infrastructure budget forecast

Outcome

A mature engineering organization ready for due diligence and the next funding round.

3-month minimum commitment