DATADATA
Senior data leadership that owns your metrics
Hands-on data strategy, analytics, and infrastructure for teams that need clarity, not consultants. We build systems of record with authoritative data that your team, your tools, and AI agents can trust.
About
I believe in numbers you can trust and metrics that drive decisions.
My background includes building and leading analytics teams at scale, serving as fractional Head of Analytics for major e-commerce platforms, and architecting operational systems that significantly reduced administrative workload for service businesses.
I work with AI agents daily for coding, research, and analysis—which reinforces a key insight: AI delivers value when it can trust the data it operates on. Solid data infrastructure isn't optional anymore.
Most data work fails because it's disconnected from business outcomes. I build data systems that leadership actually uses—not dashboards that sit unused, or pipelines that break in production.
I work directly with founders and senior operators because that's where decisions happen. No layers, no handoffs, no consultants who disappear after the strategy deck.
If I take on your metrics, I own them. That means I care whether they improve, and I'll tell you when something isn't working.
Problems I Solve
Unclear or conflicting metrics
Different teams report different numbers. You need a single source of truth and clear definitions everyone trusts.
Data infrastructure that doesn't scale
Your analytics setup worked at 10x, but it's breaking at 100x. You need architecture that grows with you.
Analytics as an afterthought
Data decisions happen in spreadsheets or gut calls. You need data systems that inform strategy, not just report on it.
Missing ownership of data outcomes
No one truly owns whether metrics improve. You need someone who treats your numbers like their own.
Data quality issues that compound at scale
When automated systems and AI tools operate on your data, quality issues multiply. You need authoritative data sources that both humans and systems can trust—AI delivers value when it can rely on the data.
Core Service Pillars
Data ownership & metric definition
Establish clear, business-aligned metrics with documented definitions and ownership. Ensure everyone trusts the numbers.
Analytics & BI systems
Build or improve dashboards, reporting pipelines, and self-service analytics that teams actually use.
Data infrastructure & pipelines
Design and implement reliable data pipelines, warehousing, and ETL processes that scale with your business. Build systems of record that serve as authoritative data sources—the foundation that makes AI tools and agents effective.
Marketing & growth analytics
Connect marketing spend to revenue, optimize funnels, and build attribution models that inform decisions.
Operational & funnel analytics
Track and improve conversion rates, operational efficiency, and key business processes with data-driven insights.
Who It's For / Who It's Not For
For
- ✓Teams that need clarity on what metrics matter and how to measure them
- ✓Venture-backed startups needing fractional Head of Analytics
- ✓SMBs replacing spreadsheets with operational systems
- ✓Companies scaling past early-stage data chaos
- ✓Teams with complex conversion funnels (e.g. two-sided marketplaces) needing clear measurement
- ✓Founders and operators who want direct access to senior data leadership
- ✓Organizations ready to invest in data as a strategic capability
Not For
- ✗Teams looking for junior analysts or offshore development
- ✗Projects that need large teams or agency-style resourcing
- ✗Companies that want data work without business context or ownership
- ✗Organizations not ready to commit to data as a priority
Case Snapshots
Problem
A service business was drowning in spreadsheets—manual registration, attendance, billing, and no way to see what was really happening.
Intervention
Designed and deployed an end-to-end management platform replacing fragmented tools with a unified system: registration, attendance, billing, and real-time analytics dashboards for non-technical owners.
Outcome
Significant reduction in administrative workload; real-time reporting enabling data-driven decisions without spreadsheets.
Problem
A national competition needed to manage clients, registrations, fees, and documents—all handled manually across multiple systems.
Intervention
Architected a comprehensive relational system covering client management, registration, fee tracking, document generation, and bilingual (Hebrew/English) email automation.
Outcome
Full operational system replacing manual processes; integration-ready for external automation.
Problem
A major e-commerce platform needed executive-level analytics ownership across product, marketing, and operations—with no single source of truth.
Intervention
Embedded as fractional Head of Analytics; built real-time dashboards, KPI frameworks, and A/B tests for buyer and seller conversion funnels. Translated C-suite priorities into measurable analytics frameworks.
Outcome
Single source of truth for executive decisions; findings presented directly to founders and leadership.
Problem
Revenue and GMV calculations were producing wrong results; database sorting issues were causing data inconsistencies in production.
Intervention
Diagnosed root cause (database maintenance processes affecting sort keys); fixed calculation logic and established data pipeline transparency with prioritized task lists for stakeholders.
Outcome
Production data issues resolved; reduced manual work for stakeholders; team clarity on data priorities.
Tools & Stack
Databases & Warehouses
- Redshift
- Postgres
- Snowflake
BI & Visualization
- Tableau
- Looker
- Redash
- Google Looker Studio
Data & Modeling
- SQL
- Python
- ETL/ELT
- Data modeling
Operational Systems
- Airtable
- CRM
- Workflow automation
- APIs
Analytics
- A/B testing
- Experimentation
- Cohort analysis
AI & Collaboration
- AI-assisted workflows
- LLM research tools
- AI integration
Additional Skills & Experience
Data & Analytics Capabilities
- •Built advanced dashboards and analytics: cohort analysis, lifecycle analytics, auction extension metrics, deferred revenue tracking
- •Diagnosed and resolved complex database issues (sort keys, calculation logic) in production environments
- •Implemented data pipeline transparency and prioritized task systems for stakeholders
- •Expert in Looker, SQL, data modeling, cohort analysis, financial analytics, and marketing attribution
Get in touch
If this sounds like a fit, let's talk. I work with a small number of clients at a time, so conversations start with understanding your specific needs.