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Key Responsibilities and Required Skills for Business Analytics Intern

💰 $18/hr - $40/hr

Business AnalyticsDataInternshipBIAnalytics

🎯 Role Definition

The Business Analytics Intern supports cross-functional teams by collecting, cleansing, analyzing, and visualizing data to inform business decisions. This role combines hands-on data work (SQL, Excel, Python/R, and BI tools) with stakeholder communication to deliver actionable insights, dashboards, and automated reporting. The Business Analytics Intern contributes to KPI tracking, A/B test analysis, forecasting, and ad-hoc analytic requests while learning data engineering and product analytics fundamentals.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Science / Analytics coursework and capstone projects or university-level research assistant roles.
  • Related internships such as Marketing Intern, Operations Intern, or Finance Intern with exposure to data and reporting.
  • Entry-level roles like Reporting Analyst, Junior Data Analyst, or Customer Insights Intern.

Advancement To:

  • Business Analyst
  • Data Analyst
  • Analytics Engineer or BI Developer
  • Product Analyst or Growth Analyst
  • Data Scientist (with additional technical development)

Lateral Moves:

  • Marketing Analytics Specialist
  • Financial Planning & Analysis (FP&A) Analyst
  • Operations Research Analyst
  • Customer Insights or CRM Analyst

Core Responsibilities

Primary Functions

  • Design, build, and maintain interactive dashboards and visualizations using Power BI, Tableau, or Looker to provide weekly and monthly KPIs to product, marketing, and executive stakeholders; iterate rapidly based on stakeholder feedback to improve clarity and business impact.
  • Write, test, and optimize SQL queries to extract and aggregate data from transactional databases and data warehouses (e.g., BigQuery, Redshift, Snowflake) to support recurring reports, cohort analyses, and one-off investigations.
  • Clean, validate, and transform raw data using Python (pandas), R, or Excel to ensure accurate analysis and reliable metrics feeding BI dashboards and executive reporting.
  • Perform exploratory data analysis and statistical summarization to identify trends, seasonality, anomalies, and opportunities for growth or cost reduction; present findings with actionable recommendations and clear next steps.
  • Support the design, analysis, and interpretation of A/B tests and feature experiments including test setup validation, statistical significance testing, and translating results into product recommendations.
  • Develop automated ETL workflows and scheduled reporting (cron jobs, Airflow tasks, or built-in BI scheduling) to reduce manual effort, improve report timeliness, and ensure reproducible analytic processes.
  • Collaborate with product managers, marketers, finance, and engineering to translate business questions into analytic requirements, define success metrics, and implement measurement frameworks for new initiatives.
  • Build and maintain data dictionaries, measurement guides, and documentation for metrics, data sources, joins, and transformations to improve team onboarding and data governance.
  • Conduct segmentation and cohort analyses to uncover user behavior patterns, retention drivers, and high-value customer profiles that inform lifecycle marketing and product prioritization.
  • Create ad-hoc quantitative models and forecasts (time series, regression, or simple machine learning prototypes) to estimate revenue impacts, user growth, churn risk, and capacity planning scenarios.
  • Monitor data quality and perform root-cause analysis for discrepancies in pipelines or reporting; triage issues with data engineering and source system owners to restore accuracy quickly.
  • Extract actionable insights from web and mobile analytics platforms (Google Analytics, Firebase, Mixpanel) to optimize acquisition funnels, conversion rates, and user engagement metrics.
  • Support pricing and revenue analyses by combining product usage, transaction data, and experiments to model pricing scenarios and revenue sensitivity to feature changes.
  • Produce slide decks and executive summaries that translate complex analyses into concise business implications and prioritized recommendations for leadership reviews.
  • Assist in building standardized KPI scorecards and monthly business reviews tying team activities to topline metrics and OKRs to increase cross-functional alignment and data-driven decision making.
  • Participate in sprint planning with analytics, data engineering, and product teams to scope analytic work, estimate effort, and prioritize deliverables that drive measurable business outcomes.
  • Prototype and validate predictive signals (e.g., propensity to churn or buy) using supervised learning techniques and feature engineering, handing off models or insights to engineering for productionization when appropriate.
  • Support compliance and data privacy initiatives by helping document data lineage, access controls, and ensuring analyses use approved, anonymized datasets when required.
  • Perform competitive benchmarking and market analysis combining public datasets and internal metrics to identify product differentiation opportunities and go-to-market priorities.
  • Drive continuous improvement by identifying repetitive manual reporting tasks and implementing automation, templates, or internal tools to increase team productivity and analytical throughput.
  • Liaise with cross-functional teammates to gather requirements, set expectations for delivery timelines, and ensure analytic outputs meet business needs and decision-making cadence.

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.
  • Assist in onboarding new interns and creating training materials for common analytic workflows.
  • Help maintain analytics environment and tooling, including managing credentials, dataset access requests, and documentation updates.
  • Support quality assurance for dashboards and reports by writing test cases and verifying visualized metrics against source queries.
  • Track and report progress on analytic projects, update backlog items, and escalate blockers to leads or managers.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong proficiency in SQL for complex joins, window functions, aggregations, and performance-conscious query design against data warehouses (BigQuery, Snowflake, Redshift).
  • Advanced Excel skills including pivot tables, VLOOKUP / XLOOKUP, INDEX/MATCH, advanced formulas, and basic macros for rapid prototyping and ad-hoc analysis.
  • Experience building dashboards and visualizations in Power BI, Tableau, Looker, or equivalent BI tools, with a focus on clear storytelling and usability.
  • Familiarity with Python (pandas, numpy, matplotlib/seaborn) or R for data manipulation, statistical analysis, and lightweight modeling.
  • Basic understanding of ETL concepts, data pipelines, and workflow orchestration tools (Airflow preferred) to support pipeline troubleshooting and automation.
  • Experience with web/mobile analytics tools such as Google Analytics, Firebase, Mixpanel, or Amplitude for product and funnel analyses.
  • Knowledge of basic statistical concepts: hypothesis testing, confidence intervals, p-values, regressions, and experiment design (A/B testing).
  • Experience querying or working with cloud data warehouses (BigQuery, Redshift, Snowflake) and familiarity with table partitioning and cost-aware querying.
  • Familiarity with Git or version control for code and SQL repository management; ability to produce reproducible analysis.
  • Exposure to data modeling and dimensional modeling concepts (star schema, fact and dimension tables) to support building reliable BI structures.
  • Basic experience with data visualization best practices, dashboard performance optimization, and UX considerations for non-technical stakeholders.
  • Familiarity with APIs, CSV/JSON ingestion, and basic scripting for automated data pulls and dataset refreshes.
  • Knowledge of business metrics and KPIs such as MRR/ARR, CAC, LTV, churn, conversion rate, ARPU, and retention metrics.

Soft Skills

  • Strong written and verbal communication skills, with the ability to craft concise executive summaries and present technical findings to non-technical stakeholders.
  • Curiosity and a strong problem-solving mindset; proactively asks the right questions, triangulates data sources, and surfaces root causes.
  • Attention to detail and a commitment to data accuracy, reproducibility, and high-quality deliverables.
  • Collaborative team player with the ability to work cross-functionally across product, engineering, marketing, and finance.
  • Time management and prioritization skills in a fast-paced environment, balancing multiple competing analytic requests and deadlines.
  • Business acumen with the ability to translate analytics into revenue, cost, and growth implications for stakeholders.
  • Adaptability and eagerness to learn new tools, frameworks, and domain knowledge quickly during the internship.
  • Presentation and storytelling ability: can convert complex analyses into digestible narratives and actionable recommendations.
  • Initiative and ownership mentality: takes responsibility for projects from scoping through delivery and follow-up.
  • Ethical judgment and respect for data privacy, governance, and confidentiality when handling sensitive information.

Education & Experience

Educational Background

Minimum Education:

  • Currently enrolled in or recently graduated from a Bachelor's degree program (preferredly within the last 12 months) in a quantitative or business-related field.

Preferred Education:

  • Pursuing or completed a Bachelor’s or Master’s degree in Business Analytics, Data Science, Statistics, Economics, Computer Science, Information Systems, Finance, Mathematics, or Engineering.

Relevant Fields of Study:

  • Business Analytics
  • Data Science
  • Statistics
  • Economics
  • Computer Science
  • Information Systems
  • Finance
  • Mathematics
  • Engineering

Experience Requirements

Typical Experience Range: 0 — 2 years (internships, co-ops, university projects, and relevant coursework count)

Preferred:

  • 1+ internship or project experience working with SQL, Excel, and at least one BI tool (Tableau, Power BI, Looker).
  • Practical coursework or capstone projects involving A/B testing, forecasting, time series analysis, or predictive modeling.
  • Exposure to cloud data warehouses or ETL workflows and familiarity with Git-based workflows.
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