Torchora
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Key Responsibilities and Required Skills for Business Insights Manager

💰 $90,000 - $150,000

Business IntelligenceAnalyticsDataStrategy

🎯 Role Definition

The Business Insights Manager leads cross-functional analytics efforts to turn raw data into strategic recommendations that drive business growth and operational excellence. This role synthesizes quantitative analysis, data engineering partnerships, and compelling storytelling to influence product, marketing, finance, and executive-level decisions. The successful candidate will design and maintain KPI frameworks, own dashboard delivery, translate business goals into analytics roadmaps, and mentor a small team of analysts while ensuring analytical rigor and data governance.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Business Analyst or Senior Data Analyst with demonstrated impact on product/finance/marketing decisions.
  • Analytics Manager or BI Lead with experience managing dashboards and cross-functional stakeholders.
  • Product Analyst or Growth Analyst responsible for experimentation and funnel optimization.

Advancement To:

  • Director of Analytics / Director of Business Insights — leading multiple analytics teams and strategy for a business unit.
  • Head of Insights or Head of Data & Analytics — owning company-wide analytics priorities and data monetization.
  • VP of Data & Analytics — executive leadership role shaping data strategy and organization-wide metrics.

Lateral Moves:

  • Product Manager (data-focused) — applying analytics expertise to product strategy and roadmap.
  • Growth or Marketing Analytics Lead — focusing on acquisition, activation, and retention analytics.

Core Responsibilities

Primary Functions

  • Lead the end-to-end design, development, and maintenance of executive and operational dashboards (Tableau, Power BI, Looker) that provide actionable insights into revenue, retention, acquisition, and operational KPIs, ensuring dashboards are performant, documented, and trusted by business partners.
  • Define, own, and socialize the company-wide KPI framework and metric definitions (single source of truth), working with finance, product, marketing, and operations to ensure alignment and prevent metric drift.
  • Drive cross-functional analytics projects from scoping to delivery — translating business problems into analytical plans, prioritizing work, coordinating with data engineering for reliable data pipelines, and delivering clear recommendations tied to measurable outcomes.
  • Conduct deep-dive analyses to uncover root causes of business changes (churn, conversion drop-offs, pricing impacts), quantify opportunity and risk, and recommend prioritized, evidence-based actions to product and commercial teams.
  • Partner with data engineering and product teams to specify data requirements, advocate for instrumentation and event tracking, oversee ETL/ELT processes, and validate data quality to ensure analytics outputs are accurate and reproducible.
  • Oversee statistical analyses and experimentation (A/B tests), including hypothesis design, sample size estimation, metric selection, pre-post analysis, and communicating test learnings and business impact to product and marketing teams.
  • Build and maintain forecasting and predictive models (revenue, demand, churn) using SQL and Python/R, and collaborate with stakeholders to convert model outputs into business plans and OKRs.
  • Manage and mentor a team of analysts — setting priorities, conducting performance reviews, providing career development plans, and ensuring analytical best practices (code review, documentation, reproducibility).
  • Translate complex analytical findings into clear, succinct executive-level presentations and narratives that include recommendation, impact, and next steps to influence strategic decision-making.
  • Implement and maintain data governance around metrics, ensuring version control, lineage tracking, and a documented metrics catalog to reduce ambiguity in reporting and decision-making.
  • Lead cross-functional measurement initiatives for new product launches, pricing experiments, and go-to-market campaigns — define success metrics, tag events, and measure both short-term and long-term impacts.
  • Partner with commercial teams (sales, partnerships, customer success) to provide analytical support for sales forecasting, territory planning, commission modeling, and retention strategies.
  • Drive automation of recurring reports and analytics workflows via SQL templating, dashboards, and data productization to reduce manual reporting and scale insights delivery.
  • Advocate for data literacy across the organization by delivering training sessions, office hours, and creating self-service templates that empower business users to query and interpret data independently.
  • Evaluate, select, and help operationalize analytics tools (BI platforms, metric stores, experimentation platforms) in collaboration with IT and data engineering, balancing cost, scalability, and usability.
  • Conduct cohort analyses and lifecycle studies to identify user segments with high LTV, determine key activation points, and recommend product enhancements to improve engagement and monetization.
  • Quantify the business impact of strategic initiatives and produce ROI analyses, ensuring stakeholders understand the expected vs. realized value of investments.
  • Establish rigorous process for data validation and anomaly detection; proactively monitor pipelines and reported metrics to identify breaks or unexpected changes and coordinate rapid remediation.
  • Serve as analytics partner on strategic cross-functional initiatives (M&A diligence, international expansion, pricing strategy), providing rapid, high-quality analyses that inform leadership decisions.
  • Drive the creation and maintenance of standardized analytical SQL libraries, metric definitions, and reusable model components to ensure consistency and accelerate delivery across the analytics organization.
  • Facilitate stakeholder workshops to define business requirements, establish priorities, and align analytics roadmaps with company OKRs and strategic objectives.
  • Ensure compliance with data privacy and regulatory requirements (GDPR, CCPA) in analytics practices, working with legal and security teams to minimize risk.

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: ability to write performant, production-grade queries, optimized joins, window functions, CTEs, and query plans for large datasets.
  • BI Tools: expert-level experience building dashboards and visualizations in Tableau, Power BI, Looker, or equivalent with strong UX and performance optimization skills.
  • Statistical Analysis & Experimentation: proficiency in hypothesis testing, A/B test design, power analysis, difference-in-differences, and regression techniques.
  • Data Modeling & Metric Engineering: experience designing semantic layers, star schemas, dimensional models, and a single source of truth for metrics (dbt, semantic layer concepts).
  • Scripting & Data Analysis: working knowledge of Python or R (pandas, numpy, scikit-learn) for data transformation, modeling, and automation.
  • Data Warehousing & Cloud Analytics: familiarity with modern data stacks like BigQuery, Snowflake, Redshift, and ETL/ELT tools (Fivetran, Stitch, Airflow, dbt).
  • Forecasting & Predictive Modeling: experience building time-series models and predictive models for revenue, demand, and churn (ARIMA, Prophet, gradient boosting).
  • SQL-Based BI/Reporting Automation: ability to create parameterized reports, scheduled jobs, and alerting for metric anomalies.
  • Data Governance & Privacy: understanding of data lineage, access controls, metric versioning, and privacy frameworks (GDPR/CCPA implications for analytics).
  • Product & Marketing Analytics: hands-on experience measuring funnels, attribution, LTV, cohort retention, CAC, and unit economics.
  • Familiarity with experimentation platforms and analytics SDKs (Optimizely, Amplitude, Mixpanel) is a plus.

Soft Skills

  • Business acumen: translates analytics into business cases and prioritizes work by expected impact and feasibility.
  • Storytelling & Communication: crafts compelling narratives and presentations for senior leaders with clear recommendations and quantified impact.
  • Stakeholder Management: builds trust with cross-functional partners, negotiates priorities, and balances competing requests.
  • Leadership & Mentorship: develops analytical talent, provides feedback, and fosters a high-quality analytic culture.
  • Strategic Thinking: connects data insights to long-term strategy and operational goals.
  • Problem Solving: breaks down ambiguous problems into testable hypotheses and iterative experiments.
  • Project Management: plans, scopes, and drives complex analytics projects to on-time delivery.
  • Collaboration & Influence: works effectively with product, engineering, finance, and operations to align on objectives and deliverables.
  • Attention to Detail: ensures accuracy in metric calculation, documentation, and reporting.
  • Adaptability: thrives in fast-paced environments and adjusts priorities amid changing business needs.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Business Analytics, Economics, Statistics, Computer Science, Data Science, Finance, or related quantitative field.

Preferred Education:

  • Master's degree (MS/MA) in Data Science, Analytics, Economics, Statistics, or an MBA with a quantitative concentration preferred for senior hires.

Relevant Fields of Study:

  • Business Analytics
  • Data Science / Statistics
  • Economics
  • Computer Science
  • Finance
  • Applied Mathematics

Experience Requirements

Typical Experience Range:

  • 5–9 years of progressive analytics or BI experience, including 2+ years leading analysts or managing analytics projects.

Preferred:

  • 7+ years of analytics experience with demonstrated ownership of metric frameworks, BI tooling, cross-functional program leadership, and people management experience. Experience hiring and scaling an insights function and working with modern cloud data stacks is strongly preferred.
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