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Key Responsibilities and Required Skills for Business Intelligence Analyst

💰 $70,000 - $110,000

Business IntelligenceData AnalyticsAnalyticsReporting

🎯 Role Definition

The Business Intelligence Analyst is responsible for turning raw data into actionable insights that inform business decisions, optimize performance, and drive revenue. This role designs, develops, and maintains dashboards, reports, and data models; partners with cross-functional stakeholders to define KPIs and reporting requirements; ensures data quality and governance; and automates reporting workflows to enable data-driven decision-making across the organization.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst
  • Reporting Analyst
  • Business Systems Analyst

Advancement To:

  • Senior Business Intelligence Analyst
  • BI Manager / Analytics Manager
  • Director of Business Intelligence / Head of Analytics
  • Product Analytics Manager or Data Product Manager

Lateral Moves:

  • Data Scientist
  • Data Engineer
  • Product Analyst
  • Analytics Consultant

Core Responsibilities

Primary Functions

  • Develop, maintain and optimize interactive dashboards and executive-level reports using Power BI, Tableau, or Looker to deliver timely insights to product, marketing, finance, and operations teams.
  • Design and implement robust data models and dimensional schemas in the data warehouse (e.g., Snowflake, Redshift, BigQuery) to support performant self-service analytics and standardized reporting.
  • Author complex, efficient SQL queries and stored procedures to extract, transform, and aggregate data for recurring and ad-hoc analyses, ensuring query performance and maintainability.
  • Translate business requirements into technical specifications by collaborating with stakeholders to define KPIs, business rules, and acceptance criteria for reports and dashboards.
  • Build and maintain ETL/ELT pipelines (using tools like dbt, SSIS, Airflow, or Informatica) to reliably ingest, clean, and transform source-system data for analytical consumption.
  • Create and maintain automated reporting schedules and alerting mechanisms that notify stakeholders of anomalies, trends, or KPI deviations in near real-time.
  • Conduct deep-dive analyses to uncover root causes of business performance issues, provide recommendations, and quantify potential business impact with clear, data-driven narratives.
  • Validate data accuracy through reconciliation checks, anomaly detection, lineage validation, and by establishing and enforcing data quality rules and monitoring.
  • Implement and document semantic layers, data dictionaries, and metric definitions to ensure consistent usage of terms, measures, and calculations across teams.
  • Partner closely with data engineering to design scalable data architectures, optimize query performance, and prioritize schema changes that support analytics use cases.
  • Lead cross-functional requirements gathering sessions and workshops to align stakeholders on reporting priorities, success metrics, and delivery timelines.
  • Design A/B test measurement plans and analyze experiment results to provide statistically sound recommendations that inform product and marketing decisions.
  • Create predictive and forecasting models using time-series analysis and regression where appropriate, and translate model outputs into actionable business recommendations.
  • Optimize report performance by applying best practices for visualization design, indexing strategies, partitioning, and caching to improve user experience and reduce compute costs.
  • Implement security and access controls on BI platforms and datasets to ensure confidentiality and compliance with company policies and data privacy regulations.
  • Mentor junior analysts by conducting code reviews, sharing analytics best practices, and providing training on BI tools, SQL, and data interpretation.
  • Maintain documentation for analytics processes, data pipelines, transformation logic, and reporting catalogues to ensure operational continuity and onboarding effectiveness.
  • Collaborate with finance and sales teams to design attribution models, revenue reporting, and operational dashboards that support forecasting and budgeting cycles.
  • Evaluate and integrate third-party data sources and vendor tools into the analytics ecosystem, managing data contracts, quality assessment, and ingestion strategies.
  • Drive continuous improvement by identifying automation opportunities to replace manual reporting tasks with scalable, repeatable analytics solutions.
  • Translate complex analytical findings into concise executive summaries, slide decks, and visualizations that are easily understandable by non-technical audiences.
  • Ensure alignment between product, marketing, and operations KPIs by facilitating regular analytics review meetings and tracking roadmap delivery for high-impact initiatives.
  • Monitor industry best practices in BI, analytics tooling, and data governance and recommend adoption of modern approaches that increase speed-to-insight and reduce technical debt.

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.
  • Provide training and documentation to business users to promote adoption of self-service BI capabilities and best practices.
  • Assist in vendor evaluations, proof-of-concepts, and migrations for analytics platforms and data warehousing technologies.
  • Participate in data governance committees to establish ownership, stewardship, and lifecycle management of critical datasets.
  • Help establish SLAs for report delivery, dashboard uptime, and data refresh cadence to meet stakeholder expectations.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL (window functions, CTEs, performance tuning) for complex data extraction and transformation.
  • Strong experience with BI visualization tools such as Power BI, Tableau, Looker, or Qlik.
  • Data modeling and dimensional design (star schemas, fact and dimension modeling) for analytical data stores.
  • Familiarity with cloud data warehouses: Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse.
  • ETL/ELT pipeline development using tools like dbt, Airflow, SSIS, Talend, or Informatica.
  • Experience with scripting and analytics in Python or R for data manipulation, analysis, and automation.
  • Proficiency in DAX, MDX or advanced calculation languages specific to Power BI/SSAS/Tableau for measure creation.
  • Knowledge of data governance, data lineage, master data management, and data quality frameworks.
  • Strong Excel skills (pivot tables, Power Query, advanced formulas) for ad-hoc analyses and validation.
  • Familiarity with version control (Git), CI/CD concepts for analytics code, and basic deployment pipelines.
  • Understanding of statistics, hypothesis testing, A/B testing, and forecasting techniques.
  • Experience integrating and modeling third-party data (CRM, advertising platforms, SaaS product metrics).
  • Basic familiarity with cloud platforms and managed services (AWS, GCP, Azure) and cost-aware analytics design.

Soft Skills

  • Excellent stakeholder management and ability to translate business goals into analytic solutions.
  • Clear written and verbal communication; experienced presenting insights to executives and cross-functional teams.
  • Strong problem-solving and critical-thinking skills with a results-oriented mindset.
  • Attention to detail and methodological rigor in validating data and analysis.
  • Ability to prioritize competing requests and manage time effectively in a fast-paced environment.
  • Collaborative teamwork across product, engineering, finance, and marketing organizations.
  • Data storytelling: turning complex analyses into actionable recommendations and narratives.
  • Adaptability and continuous learning mindset to keep up with evolving analytics tools and best practices.
  • Mentoring and coaching skills to grow junior analytics talent.
  • Project management and organizational skills to deliver analytics projects end-to-end.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Statistics, Mathematics, Economics, Business Analytics, or related field.

Preferred Education:

  • Master's degree in Data Science, Analytics, Business Intelligence, Statistics, or an MBA with a data focus.
  • Professional certifications such as Microsoft Certified: Data Analyst Associate, Tableau Desktop Specialist, Google Cloud Certified - Professional Data Engineer, or dbt Fundamentals.

Relevant Fields of Study:

  • Computer Science
  • Information Systems
  • Statistics & Applied Mathematics
  • Economics
  • Business Analytics
  • Engineering (Industrial, Systems)

Experience Requirements

Typical Experience Range:

  • 2–5 years of hands-on experience in BI, analytics, or reporting roles working with cross-functional business stakeholders.

Preferred:

  • 3–7+ years of progressive experience building dashboards, data models, and pipelines in a cloud-based analytics stack; demonstrated ability to lead analytics projects and mentor junior analysts.
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