Key Responsibilities and Required Skills for Head of Analytics

🎯 Role Definition

The Head of Analytics is a pivotal leadership position responsible for shaping and executing the company's overall data analytics vision and strategy. This individual will lead a multi-disciplinary team of analysts and BI professionals to transform raw data into actionable insights that drive strategic decision-making, operational efficiency, and revenue growth. Reporting to a C-level executive (such as the CDO, CTO, or COO), the Head of Analytics acts as the primary champion for data literacy and a data-informed culture throughout the organization, ensuring that analytics capabilities are a core component of our competitive advantage.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Analytics Manager
Principal Data Scientist
Director of Business Intelligence
Senior Strategy Consultant

Advancement To:

Chief Data Officer (CDO)
VP of Data & Analytics
VP of Strategy & Operations
Chief Operating Officer (COO)

Lateral Moves:

Head of Data Science
Head of Product (Data-Focused)
Head of Data Governance

Core Responsibilities

Primary Functions

Develop, own, and execute the comprehensive, long-term analytics strategy and roadmap, ensuring tight alignment with overarching business goals and executive priorities.
Lead, mentor, and scale a high-performing team of data analysts, BI developers, and analytics managers, fostering a collaborative culture of analytical rigor, innovation, and continuous professional development.
Act as a strategic partner to C-suite and senior leadership, translating complex business challenges into clear analytical frameworks and delivering data-driven recommendations to guide top-level strategy.
Champion the establishment and evangelization of a data-driven culture across all departments, empowering teams with the tools, training, and confidence to leverage data in their daily work.
Oversee the design, development, and maintenance of our core Business Intelligence infrastructure, including key dashboards, reporting suites, and self-service analytics platforms (e.g., Tableau, Power BI).
Define, track, and analyze the North Star metrics and Key Performance Indicators (KPIs) that measure business health, product performance, and operational effectiveness.
Translate complex analytical findings and statistical models into compelling narratives and clear, actionable insights for non-technical stakeholders through exceptional data storytelling.
Direct the full lifecycle of major analytical projects, from initial requirements gathering and hypothesis formulation through to data modeling, analysis, and final presentation of results.
Manage the departmental budget, including technology stack licensing, headcount planning, resource allocation, and relationships with external data vendors and consultants.
Establish and enforce best practices for data quality, data governance, and analytics processes to ensure the accuracy, reliability, and security of all business reporting.
Drive the analytical deep-dives required for key business initiatives, such as market entry analysis, customer segmentation, pricing optimization, and churn prediction.
Collaborate closely with Data Engineering and IT teams to define data requirements and ensure the data warehouse architecture effectively supports the evolving needs of the analytics function.
Proactively identify and explore new opportunities for business growth and process optimization by mining vast datasets for untapped trends, patterns, and insights.
Standardize the methodologies for A/B testing, experimentation, and statistical analysis to ensure the scientific validity of our product and marketing initiatives.
Stay at the forefront of industry trends, emerging technologies, and new methodologies in the analytics and data science space to continuously enhance the team's capabilities.
Build strong, collaborative relationships with leaders in Product, Marketing, Sales, Finance, and Operations to deeply understand their challenges and embed analytics into their workflows.
Present key findings, progress against goals, and strategic recommendations to the executive team and Board of Directors on a regular basis.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis to quickly answer pressing, time-sensitive business questions from across the organization.
Contribute to the organization's broader data strategy and roadmap, providing the analytics perspective on data acquisition, governance, and architecture.
Collaborate with business units to translate their strategic data needs and reporting aspirations into clear, prioritized requirements for the data engineering and BI teams.
Participate in sprint planning and agile ceremonies within the broader data organization to ensure alignment and efficient execution of cross-functional projects.
Evaluate and recommend new tools, technologies, and platforms that can improve the efficiency and impact of the analytics team's work.
Develop and deliver data literacy training programs for business users to increase their comfort and proficiency with self-service analytics tools.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: Expert-level ability to write complex, highly-optimized SQL queries across large, intricate datasets.
Business Intelligence Tools: Deep, hands-on expertise in developing and managing enterprise-level BI platforms like Tableau, Power BI, or Looker.
Programming & Statistical Languages: Strong proficiency in a language used for data analysis such as Python (with pandas, NumPy, scikit-learn) or R.
Statistical Analysis & Modeling: Solid understanding of statistical concepts, experimental design (A/B testing), and common modeling techniques (e.g., regression, clustering, forecasting).
Data Warehousing Concepts: Strong knowledge of data modeling, ETL processes, and modern data warehouse architectures (e.g., Snowflake, BigQuery, Redshift).
Cloud Data Platforms: Experience working with major cloud ecosystems like AWS, GCP, or Azure and their associated data services.

Soft Skills

Strategic Leadership: The ability to define a compelling vision, build a high-performing team, and influence the direction of the entire company.
Stakeholder Management: Proven success in building trust and collaborative partnerships with senior executives and leaders across diverse business functions.
Communication & Data Storytelling: Exceptional ability to translate complex data into a clear, concise, and compelling narrative that drives action.
Business Acumen: A deep understanding of business operations, P&L management, and what drives commercial success in a competitive market.
Problem-Solving Mindset: A natural curiosity and a structured, hypothesis-driven approach to dissecting and solving ambiguous, complex business problems.

Education & Experience

Educational Background

Minimum Education:

Bachelor's Degree in a quantitative or business-related field.

Preferred Education:

Master's Degree, MBA, or PhD.

Relevant Fields of Study:

Computer Science, Statistics, Mathematics
Economics, Business Analytics, Finance, Engineering

Experience Requirements

Typical Experience Range: 10-15+ years of progressive experience in data analytics, business intelligence, or data science.

Preferred: At least 5-7 years of experience in a direct leadership capacity, managing and mentoring analytics or data-focused teams. Proven track record of building an analytics function from the ground up or significantly scaling an existing one.