Key Responsibilities and Required Skills for Juice Analyst

🎯 Role Definition

As our Juice Analyst, you are the storyteller behind the numbers. Your mission is to dive deep into our vast data ecosystem, extracting the 'juice'—the critical insights, trends, and opportunities that others might miss. You won't just run queries; you'll partner with leaders across product, marketing, and operations to answer their most challenging questions. By translating complex data into clear, compelling narratives and visualizations, you'll directly influence strategic decisions, optimize performance, and fuel our company's growth engine. This is a high-impact role for a curious and analytical mind eager to turn data into a competitive advantage.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Data Analyst
Data Coordinator
Business Analyst
Recent Graduate (STEM, Economics)

Advancement To:

Senior Juice Analyst
Analytics Manager
Business Intelligence Lead
Data Scientist

Lateral Moves:

Data Engineer
Product Manager
Marketing Analyst

Core Responsibilities

Primary Functions

Develop and maintain complex SQL queries to extract, manipulate, and analyze large, complex datasets from various sources like relational databases, data warehouses, and data lakes.
Design, build, and automate insightful, interactive, and user-friendly dashboards and reports using BI tools such as Tableau, Power BI, or Looker to visualize key performance indicators (KPIs) and business trends.
Conduct in-depth exploratory data analysis to identify significant patterns, correlations, anomalies, and opportunities for business improvement, presenting findings in a clear and compelling narrative.
Collaborate with cross-functional teams, including product, marketing, finance, and engineering, to deeply understand their data needs and deliver actionable insights that drive strategic decision-making.
Perform comprehensive A/B testing analysis, including hypothesis formulation, experiment design, statistical significance testing, and post-launch analysis, to evaluate the impact of new features or marketing campaigns.
Translate ambiguous business questions into specific analytical projects, meticulously defining the scope, methodology, data requirements, and expected deliverables for each initiative.
Develop and implement data models and contribute to ETL/ELT processes in collaboration with data engineers to ensure data quality, integrity, and accessibility for analytical purposes.
Monitor and analyze key performance metrics on a daily, weekly, and monthly basis, providing regular performance reports and highlighting significant changes or trends to key stakeholders.
Create and maintain comprehensive documentation for data sources, metrics, reports, and analytical models to ensure consistency, reproducibility, and knowledge sharing across the organization.
Present analytical findings and strategic recommendations to senior leadership and non-technical audiences in a clear, concise, and persuasive manner.
Utilize statistical methods and predictive modeling techniques to forecast future trends, identify potential risks, and estimate the quantitative impact of various business initiatives.
Cleanse, transform, and prepare large, often unstructured, datasets for analysis, systematically addressing issues of data quality, missing values, and inconsistencies to ensure accuracy.
Partner with stakeholders across the business to define, standardize, and govern key business metrics and KPIs, ensuring alignment and a single source of truth across different departments.
Automate recurring reporting and data extraction tasks using Python scripts (with libraries like Pandas, NumPy) or other automation tools to improve efficiency and reduce manual effort.
Conduct thorough root cause analysis to diagnose performance issues, understand unexpected metric fluctuations, and provide data-driven explanations for business phenomena.
Evaluate and recommend new analytical tools, technologies, and methodologies to enhance the team's capabilities and stay current with industry best practices.
Support the development of data literacy within the organization by training business users on how to effectively use self-service analytics tools and interpret data correctly.
Perform detailed cohort analysis and customer segmentation to understand user behavior, lifecycle, and lifetime value, providing insights for targeted marketing and product development.
Analyze customer feedback and user interaction data from various channels to identify pain points and opportunities for improving the overall customer experience and product usability.
Develop and refine attribution models to measure the effectiveness of various marketing channels and campaigns, helping to optimize marketing spend and strategic allocation.
Ensure compliance with data governance policies and privacy regulations (e.g., GDPR, CCPA) when handling, processing, and analyzing sensitive customer data.
Manage the full lifecycle of analytical projects from initial conception and data gathering through to in-depth analysis, dynamic visualization, and final presentation of results and recommendations.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's 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 engineering team.

Required Skills & Competencies

Hard Skills (Technical)

SQL Proficiency: Advanced ability to write complex, efficient, and optimized SQL queries to query large-scale data warehouses (e.g., Snowflake, BigQuery, Redshift).
BI & Visualization Tools: Expertise in creating compelling and interactive dashboards using tools like Tableau, Power BI, Looker, or similar platforms.
Programming Languages: Strong proficiency in Python (especially with Pandas, NumPy, Matplotlib) or R for data manipulation, automation, and statistical analysis.
Spreadsheet Mastery: Advanced skills in Microsoft Excel or Google Sheets, including pivot tables, advanced formulas, and data modeling.
Statistical Analysis: Solid understanding of statistical concepts and methods, including hypothesis testing, regression analysis, and A/B testing methodologies.
Data Warehousing Concepts: Familiarity with data modeling, ETL/ELT processes, and the architecture of modern data warehouses.
Version Control: Experience with Git for managing code and collaborating on analytical projects.

Soft Skills

Data Storytelling: Ability to translate complex data and analytical results into a clear, compelling, and actionable narrative for both technical and non-technical audiences.
Problem-Solving & Critical Thinking: A natural curiosity and a structured approach to breaking down ambiguous problems into manageable, data-driven questions.
Stakeholder Management: Excellent communication and interpersonal skills to build relationships, understand business needs, and manage expectations with various stakeholders.
Business Acumen: A strong understanding of business operations and the ability to connect data insights to tangible business impact and strategic goals.
Attention to Detail: Meticulous and thorough in data validation, analysis, and presentation to ensure accuracy and build trust in your findings.

Education & Experience

Educational Background

Minimum Education:

Bachelor's Degree in a quantitative or related field.

Preferred Education:

Master's Degree in Data Science, Analytics, Statistics, or a related discipline.

Relevant Fields of Study:

Computer Science
Statistics
Mathematics
Economics
Business Analytics
Information Systems

Experience Requirements

Typical Experience Range: 2-5 years of experience in a data analysis, business intelligence, or a similar analytical role.

Preferred: Experience in a fast-paced, tech-driven environment with a proven track record of delivering data-driven insights that influenced business decisions.