Head of Analytics

Career Guide

Leads an organization’s analytics strategy and teams to turn data into decision-ready insights. Sets measurement frameworks, oversees experimentation and BI, ensures data quality and governance, and partners with executives to drive growth, efficiency, and risk management.

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Key Responsibilities

Define analytics strategy and roadmap aligned to business goals
Build and lead high-performing analytics and BI teams
Establish KPI frameworks, metric definitions, and governance
Oversee experimentation (A/B tests) and causal analysis
Own analytics stack and self-serve BI enablement
Partner with executives to translate insights into actions
Ensure data quality, privacy, and compliance
Forecast performance and guide resource prioritization

Career Progression

Can Lead To

VP of Analytics

Chief Data Officer (CDO)

Head of Data Science

Transition Opportunities

Product Manager (Data/Growth)

Data Engineering Manager

Business Intelligence Director

Common Skill Gaps

Often Missing Skills

Causal inference and experimentation at scaleMetric governance and standardized KPI designCloud data warehousing architectureAdvanced SQL and analytics engineering (dbt)Designing self-serve BI and semantic layers

Development SuggestionsComplete an advanced experimentation/causal inference course and run A/B tests end-to-end; build a portfolio project implementing ELT + dbt models on Snowflake/BigQuery with a Looker/Tableau semantic layer and documented KPIs.

Market Intelligence Report

Head of Analytics is part of the Data Analysis category.Explore our market intelligence report to see how AI and hiring demand are shifting for these roles.

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Salary & Demand

Median Salary Range

Entry Level$150,000–$185,000

Mid Level$185,000–$230,000

Senior Level$230,000–$300,000

Growth Trend

growing | Data-driven decision-making expanding across industries

Companies Hiring

Major Employers

AmazonCapital OneWalmart

Industry Sectors

TechnologyFinancial ServicesRetail & E‑commerce

Recommended Next Steps

Complete a specialization in A/B testing and causal inference (e.g., Coursera/HarvardX) and apply it to a real dataset.

Build an end-to-end analytics stack project: ingest data (Fivetran/Airbyte), model with dbt, host on Snowflake/BigQuery, and publish executive dashboards.

Pursue CAP certification or an MS in Analytics/Statistics and join data leadership forums (e.g., Data Leaders, Locally Optimistic) to network and benchmark practices.