Key Responsibilities and Required Skills for Manager – Metrics, Data & Performance

🎯 Role Definition

This role requires a visionary and results-oriented Manager of Metrics to join our dynamic team. This pivotal role is at the heart of our decision-making process, responsible for architecting the framework that measures our success and illuminates our path forward. You will lead a talented team of analysts, own the entire lifecycle of our key performance indicators (KPIs), and partner with executive leadership to translate complex data into actionable, strategic insights. If you are passionate about telling stories with data, mentoring high-performing teams, and driving business impact through analytical rigor, this is the opportunity for you to shape our company's data-driven culture.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Data Analyst / Lead Analyst
Business Intelligence (BI) Lead / Developer
Data Scientist
Senior Product Analyst

Advancement To:

Director of Analytics / Business Intelligence
Senior Manager, Data Strategy
Head of Data Science

Lateral Moves:

Senior Manager, Business Operations
Product Manager, Data Products

Core Responsibilities

Primary Functions

Strategic KPI Development: Define, implement, and govern the company-wide hierarchy of Key Performance Indicators (KPIs) and North Star metrics, ensuring tight alignment with executive-level business objectives and strategic goals.
Team Leadership & Mentorship: Lead, mentor, and develop a high-performing team of data analysts, fostering a culture of curiosity, analytical excellence, and continuous professional growth.
Executive Reporting & Storytelling: Develop and present compelling narratives, visualizations, and executive-level dashboards that clearly communicate performance trends, business drivers, and the "so what" behind the data.
Deep-Dive Analysis: Spearhead complex, in-depth analytical projects to uncover root causes of performance changes, identify untapped opportunities, and investigate critical business questions.
Forecasting & Goal Setting: Partner with Finance and Operations to build robust forecasting models and establish data-informed goals and targets for various business units.
Data Infrastructure Collaboration: Serve as the primary business liaison to Data Engineering and IT, translating business requirements into technical specifications for data pipelines, data models, and warehousing needs.
Analytics Roadmap Ownership: Develop and own the strategic roadmap for the metrics and analytics function, prioritizing initiatives that deliver the highest value and business impact.
Data Democratization & Literacy: Champion data literacy across the organization by creating self-service analytics tools, providing training, and establishing best practices for data interpretation and usage.
Experimentation Framework Management: Design and oversee the company's A/B and multivariate testing framework, ensuring statistical rigor and providing clear recommendations based on experiment outcomes.
Business Performance Reviews: Lead the preparation and presentation of data for weekly, monthly, and quarterly business review (WBR/MBR/QBR) meetings with senior leadership.
Data Governance & Quality Assurance: Establish and enforce processes to maintain the accuracy, consistency, and reliability of all reported data, acting as the ultimate source of truth for business metrics.
Stakeholder Partnership: Build and maintain strong, collaborative relationships with leaders across Product, Marketing, Sales, and Operations to understand their challenges and deliver tailored analytical support.
Tooling & Technology Evaluation: Continuously evaluate, recommend, and implement new analytics technologies, tools, and methodologies to enhance the team's capabilities and efficiency.
Business Process Optimization: Utilize data analysis to identify inefficiencies in current business processes and recommend data-driven solutions for improvement and optimization.
Customer Behavior Analysis: Conduct comprehensive analysis of customer lifecycle, segmentation, and behavior patterns to inform product strategy and marketing campaigns.
Market & Competitive Intelligence: Synthesize and analyze market trends and competitive landscape data to provide a contextual backdrop for internal performance metrics.
Anomaly Detection & Investigation: Develop automated systems and manual processes to proactively detect, investigate, and explain significant anomalies or shifts in key business metrics.
Project Management: Manage the entire project lifecycle for multiple analytical initiatives simultaneously, from requirements gathering and planning to execution and delivery.
Definition Standardization: Drive consensus and create a centralized dictionary for all key business terms and metric definitions to ensure consistent language and understanding across the company.
Cross-functional Initiative Leadership: Act as the analytical lead on major cross-functional projects, ensuring that data-driven insights are embedded in the decision-making process from the outset.
Budget & Resource Planning: Manage the department's budget and resources, making strategic decisions on headcount, tooling, and training to effectively support business needs.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's broader data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data and engineering teams.
Mentor junior employees outside of your direct reporting line who are interested in analytics.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: Expertise in writing complex, highly-optimized SQL queries to extract and manipulate data from large, complex relational databases.
BI & Visualization Tools: Mastery of at least one major BI platform (e.g., Tableau, Power BI, Looker, Qlik Sense) for creating insightful and interactive dashboards.
Scripting & Statistical Languages: Proficiency in Python (with Pandas, NumPy, Scikit-learn) or R for advanced statistical analysis, data modeling, and automation.
Data Warehousing Concepts: Strong understanding of data warehouse architecture (e.g., Snowflake, BigQuery, Redshift) and principles of dimensional modeling.
ETL/ELT Processes: Solid knowledge of the principles and tools behind data extraction, transformation, and loading processes.
Statistical Analysis & Experimentation: Deep knowledge of statistical methods, hypothesis testing, and A/B/multivariate testing design and interpretation.
Data Modeling: Experience in designing and implementing data models that are scalable, efficient, and meet business requirements.
Cloud Platforms: Familiarity with major cloud environments (AWS, GCP, Azure) and their associated data services.
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Version Control Systems: Working knowledge of Git for code collaboration and version control.
Spreadsheet Proficiency: Advanced proficiency in Excel or Google Sheets for financial modeling, ad-hoc analysis, and data validation.

Soft Skills

Leadership & Mentorship: Proven ability to lead, inspire, and grow a team of technical professionals.
Storytelling with Data: Exceptional skill in translating complex data into a clear, concise, and compelling narrative for diverse audiences.
Stakeholder Management: Ability to build rapport and influence decision-making with stakeholders at all levels, from individual contributors to C-suite executives.
Strategic Thinking: The capacity to see the bigger picture, understand business strategy, and align analytical work to the most critical priorities.
Business Acumen: Strong understanding of business operations, revenue drivers, and the dynamics of the industry.
Exceptional Communication: World-class written and verbal communication skills, with a talent for simplifying the complex.
Pragmatic Problem-Solving: A structured, hypothesis-driven approach to solving ambiguous and challenging business problems.
Influence & Persuasion: The ability to advocate for a data-driven viewpoint and persuade others to take action.

Education & Experience

Educational Background

Minimum Education:

Bachelor's Degree in a quantitative or related field.

Preferred Education:

Master's Degree (MBA, MS in Analytics, Data Science, Statistics, or similar).

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

7-10+ years of progressive experience in data analytics, business intelligence, or a related field.
Minimum of 3 years of direct people management or formal team leadership experience is required.

Preferred:

Experience in a fast-paced, high-growth technology (SaaS, e-commerce, fintech) environment.
A proven track record of building an analytics or metrics function from the ground up or significantly scaling an existing one.
Demonstrated success in influencing executive-level decisions and strategy through data-driven insights.