Torchora
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Key Responsibilities and Required Skills for Lead Data Analyst

💰 $130,000 - $180,000

Data AnalyticsBusiness IntelligenceLeadershipTechnology

🎯 Role Definition

As our Lead Data Analyst, you will be the cornerstone of our data-driven strategy. You won't just be running queries; you'll be shaping the questions we ask. This is a highly visible role where you will partner with leaders across Product, Marketing, Finance, and Engineering to uncover strategic insights, optimize performance, and guide the future direction of our business. You will lead and mentor a team of analysts, empowering them to grow their skills while setting the standard for analytical excellence and innovation within the company. Your work will directly impact our bottom line, enhance our customer experience, and solidify data as a critical asset in our decision-making process.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Analyst
  • Senior Business Intelligence (BI) Analyst
  • Data Scientist

Advancement To:

  • Analytics Manager / Manager of Data Analytics
  • Director of Analytics or Business Intelligence
  • Head of Data

Lateral Moves:

  • Product Manager - Data & Insights
  • Senior Data Scientist

Core Responsibilities

Primary Functions

  • Lead, mentor, and develop a high-performing team of data analysts, fostering a culture of curiosity, innovation, and analytical rigor.
  • Define and own the analytical roadmap, collaborating with leadership to prioritize projects that align with key business objectives and drive strategic growth.
  • Act as the primary analytical partner for executive and senior leadership, translating complex business questions into clear, actionable analytical frameworks and insights.
  • Design, develop, and maintain advanced BI dashboards and reports in tools like Tableau or Power BI to track core KPIs and empower self-service analytics across the organization.
  • Conduct in-depth exploratory analysis on large, complex datasets to identify untapped opportunities, hidden trends, and critical business risks.
  • Champion data-driven decision-making by embedding analytical best practices and insights directly into the operational workflows of cross-functional teams like Product, Marketing, and Sales.
  • Oversee the entire lifecycle of A/B testing and experimentation, from hypothesis generation and experimental design to statistical analysis and result interpretation.
  • Develop sophisticated data models and statistical analyses, such as customer segmentation, lifetime value (LTV) prediction, churn forecasting, and cohort analysis.
  • Partner closely with Data Engineering to define data requirements, influence data architecture, and ensure the data pipelines and warehouse are optimized for analytical needs.
  • Establish and enforce data governance standards and best practices to ensure data quality, accuracy, consistency, and accessibility across all systems.
  • Communicate complex analytical findings and strategic recommendations to diverse audiences, including non-technical stakeholders, through compelling data storytelling and visualizations.
  • Drive the evolution of our analytics tech stack by evaluating, recommending, and implementing new tools and technologies that enhance analytical capabilities.
  • Automate recurring analyses and reports using Python, R, and SQL, freeing up team capacity to focus on higher-value strategic initiatives.
  • Perform deep-dive root cause analysis on business performance anomalies, providing clear explanations and actionable recommendations for corrective action.
  • Create and maintain comprehensive documentation for data sources, metrics, and analytical models to serve as a single source of truth for the organization.
  • Lead cross-functional projects from an analytical perspective, managing timelines, resources, and stakeholder expectations to ensure successful delivery of insights.
  • Develop a deep understanding of the business domain, market landscape, and competitive environment to provide contextual, relevant, and impactful analysis.
  • Proactively identify and scope key analytical questions that will shape the company's future strategy and product direction.
  • Standardize metric definitions and KPIs across departments to create a unified and consistent view of business performance.
  • Serve as a subject matter expert on data analysis and visualization, promoting data literacy and upskilling team members throughout the company.
  • Manage the intake and prioritization of ad-hoc data requests from across the business, balancing short-term needs with long-term strategic projects.
  • Build predictive models to forecast key business metrics and identify leading indicators of future performance.

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)

  • Expert-level proficiency in SQL, including advanced techniques like window functions, CTEs, and query optimization on large-scale datasets.
  • Strong programming skills in Python or R for data manipulation (pandas, dplyr), statistical analysis, and automation.
  • Extensive experience building complex and intuitive dashboards using BI visualization tools such as Tableau, Power BI, Looker, or Metabase.
  • Hands-on experience querying and modeling data in modern cloud data warehouses like Snowflake, Google BigQuery, or Amazon Redshift.
  • Solid understanding of statistical methods and machine learning concepts (e.g., regression, classification, clustering, A/B testing statistics).
  • Familiarity with data transformation tools and concepts, particularly with dbt (data build tool).
  • Proficiency with version control systems, especially Git, for collaborative code development and maintaining analytical projects.
  • Experience with ETL/ELT pipeline concepts and an ability to collaborate with engineering on data ingestion and modeling.
  • Knowledge of web analytics and product analytics platforms like Google Analytics, Mixpanel, or Amplitude.
  • Advanced skills in spreadsheet software (Microsoft Excel, Google Sheets) for financial modeling and ad-hoc analysis.

Soft Skills

  • Leadership & Mentorship
  • Stakeholder Management
  • Data Storytelling & Communication
  • Business Acumen & Strategic Thinking
  • Advanced Problem-Solving
  • Project Management
  • Intellectual Curiosity

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's Degree (M.S. or M.A.)

Relevant Fields of Study:

  • Computer Science, Statistics, Mathematics, Economics
  • Business Analytics, Information Systems, or a related quantitative field

Experience Requirements

Typical Experience Range: 7-10 years of progressive experience in data analytics, business intelligence, or a related role.

Preferred: At least 2-3 years of experience in a formal or informal leadership capacity, including mentoring junior analysts and leading analytical projects.

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