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

1

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.

2

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.

3

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

Contract Length:

Growth CTO

Most popular

$6,000per month

10 hours per week, 40 hours per month

Post-Seed to Series A startups actively building product and team (3 to 10 engineers).

  • Weekly call with founders (1 hour)
  • Bi-weekly sprint reviews with the team
  • Active hiring involvement: sourcing, interviews, offers, onboarding
  • Product architecture and technical roadmap ownership
  • Emergency availability for critical situations
  • Slack support (24-hour response time)

What's included:

  • Quarterly OKRs for the technical team
  • Hiring playbook for first 5-10 engineers
  • Architecture Decision Records (ADR)

Outcome:A solid technical foundation and team ready for rapid growth and product scaling without chaos.

Contract term:from 3 months

Scale CTO

$12,000per month

≈3 days per week, 80 hours per month

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

  • Everything from Growth CTO package
  • Participation in executive meetings and investor discussions
  • Building engineering management team (leads, engineering managers)
  • Due diligence support for fundraising
  • Organizational design: team structure, processes, rituals
  • Technical debt strategy and refactoring roadmap
  • Vendor and partner negotiations (AWS, third-party services, contractors)

What's included:

  • Engineering handbook and team culture code
  • Leveling and compensation framework
  • Engineering metrics dashboards (DORA, velocity, code quality, etc.)
  • Incident management and on-call rotation

Outcome:A mature engineering organization with clear processes, metrics, and a culture of product ownership.

Contract term:from 6 months

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Frequently asked questions

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Fractional CTO for AdTech and MarTech platforms - Anton Golosnichenko - Fractional CTO