Torchora
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Key Responsibilities and Required Skills for Growth Analyst

💰 $85,000 - $125,000

Data & AnalyticsMarketingProductGrowth

🎯 Role Definition

As a Growth Analyst, you will be the analytical cornerstone of our growth initiatives. You are a curious and results-oriented individual who lives at the intersection of data, product, and marketing. Your mission is to dive deep into complex datasets to uncover actionable insights about our users and their journey. You will be responsible for defining key metrics, building insightful dashboards, and running experiments that directly impact user acquisition, activation, engagement, and retention. You will not just report on numbers; you will tell the story behind them and provide strategic recommendations that will shape the future of our product and business.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst
  • Marketing Analyst
  • Business Intelligence (BI) Analyst
  • Junior Product Analyst

Advancement To:

  • Senior Growth Analyst
  • Growth Manager
  • Product Manager, Growth
  • Data Science Manager

Lateral Moves:

  • Product Analyst
  • Data Scientist
  • Marketing Operations Manager

Core Responsibilities

Primary Functions

  • Design, execute, and analyze complex A/B/n tests and multivariate experiments to optimize conversion funnels, product features, and marketing campaigns, delivering actionable insights for continuous improvement.
  • Develop and maintain comprehensive dashboards and reports in tools like Tableau, Looker, or Power BI to monitor key performance indicators (KPIs) across the entire user lifecycle (AARRR framework).
  • Conduct deep-dive analyses into user behavior patterns, segmentation, and cohort performance to identify key drivers of growth, engagement, and churn.
  • Partner with Marketing teams to measure the performance and ROI of user acquisition channels (e.g., Paid Search, SEO, Social, Email) and provide recommendations for budget allocation and strategy.
  • Translate business questions from stakeholders into analytical frameworks, and use advanced SQL to query large, complex datasets from our data warehouse (e.g., BigQuery, Snowflake, Redshift).
  • Develop predictive models to forecast user growth, lifetime value (LTV), and churn risk, enabling proactive strategies to enhance user retention.
  • Present findings and data-driven narratives in a clear and compelling manner to a variety of audiences, from executive leadership to product and engineering teams.
  • Identify and quantify new growth opportunities by analyzing market trends, competitive landscapes, and untapped user segments.
  • Collaborate with Product and Engineering teams to define data tracking requirements and ensure the integrity and accuracy of our analytics instrumentation.
  • Perform rigorous funnel analysis to identify points of friction and opportunity within the user journey, from initial awareness to conversion and advocacy.
  • Synthesize quantitative data with qualitative insights from user research, surveys, and feedback to build a holistic understanding of our users.
  • Create and manage a prioritized roadmap of experiments and analytical projects aligned with the company's overarching growth objectives.
  • Investigate anomalies in data and performance metrics to identify root causes and recommend corrective actions.
  • Build sophisticated user segmentation models based on behavior, demographics, and value to enable targeted marketing and personalized product experiences.
  • Analyze the impact of new feature launches and product changes on user behavior and core business metrics.
  • Own the definition, calculation, and validation of core growth metrics, ensuring consistency and reliability across the organization.
    s- Provide analytical support for strategic initiatives, including pricing and packaging analysis, international expansion, and new product explorations.
  • Automate recurring analyses and reports using Python, R, or other scripting languages to increase team efficiency.
  • Champion a culture of data-informed decision-making and hypothesis-driven experimentation throughout the company.
  • Monitor the health of our growth loops, identifying areas for optimization in viral, paid, and content-driven acquisition cycles.
  • Conduct thorough post-mortem analyses of both successful and failed experiments to extract learnings that inform future strategy.

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)

  • Advanced SQL: Demonstrated ability to write complex, efficient queries to extract and manipulate data from relational databases and data warehouses.
  • Data Visualization & BI Tools: Expertise in creating insightful dashboards and reports using tools such as Tableau, Looker, Power BI, or Metabase.
  • Experimentation & Statistical Analysis: Strong understanding of A/B testing principles, experimental design, and statistical concepts (e.g., significance, confidence intervals, regression).
  • Programming for Data Analysis: Proficiency in Python (with libraries like Pandas, NumPy, Matplotlib) or R for data cleaning, analysis, and visualization.
  • Product Analytics Platforms: Hands-on experience with tools like Amplitude, Mixpanel, or Heap to analyze user behavior and product usage.
  • Advanced Spreadsheet Skills: Mastery of Excel or Google Sheets for financial modeling, forecasting, and ad-hoc analysis.
  • Data Warehousing Concepts: Familiarity with modern data warehouses like Google BigQuery, Amazon Redshift, or Snowflake.

Soft Skills

  • Data Storytelling: Ability to translate complex data analysis into a clear, compelling narrative that drives action and informs strategic decisions.
  • Innate Curiosity & Hypothesis-Driven Mindset: A natural desire to ask "why" and formulate testable hypotheses to understand underlying trends and behaviors.
  • Exceptional Problem-Solving: A structured approach to breaking down ambiguous problems into manageable components and delivering analytical solutions.
  • Cross-Functional Collaboration: Proven ability to work effectively with stakeholders across marketing, product, engineering, and leadership.
  • Strong Business Acumen: The ability to connect data insights to tangible business impact and understand the broader strategic context of your work.
  • Meticulous Attention to Detail: A commitment to data accuracy and the ability to produce rigorous, error-free analysis.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's Degree in a quantitative or related field.

Preferred Education:

  • Master's Degree in a quantitative field.

Relevant Fields of Study:

  • Statistics
  • Economics
  • Computer Science
  • Mathematics
  • Business Analytics
  • Data Science

Experience Requirements

Typical Experience Range:

  • 3-5+ years of experience in a data analytics role, such as data analyst, business analyst, or a similar position.

Preferred:

  • Direct experience within a growth, product analytics, or marketing analytics team in a fast-paced tech, SaaS, B2C, or e-commerce environment is highly preferred. A proven track record of influencing product or marketing strategy through data is a significant plus.
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