Torchora
Back to Home

Key Responsibilities and Required Skills for Business Intelligence Specialist

💰 $70,000 - $120,000

Business IntelligenceData AnalyticsIT

🎯 Role Definition

A Business Intelligence Specialist (BI Specialist) transforms raw data into actionable insights that drive business decisions. This role blends technical expertise—SQL, data modeling, ETL, and visualization—with consultative skills to partner with cross-functional stakeholders, define KPIs, build and maintain dashboards and reports, and ensure the integrity and governance of analytical data. Ideal candidates are experienced in BI tools (Power BI, Tableau, Looker), data warehouses (Snowflake, Redshift, BigQuery), and modern analytics practices.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst (reporting and dashboard development)
  • Reporting Analyst / Business Reporting Specialist
  • Junior BI Developer or BI Analyst

Advancement To:

  • Senior Business Intelligence Specialist / Senior BI Analyst
  • BI Manager / Analytics Manager
  • Data Architect or Head of Business Intelligence
  • Analytics Product Owner or Director of Data & Insights

Lateral Moves:

  • Data Engineer (focus on pipelines & infrastructure)
  • Data Scientist (advanced analytics & modeling)
  • Product Analyst / Operations Analyst

Core Responsibilities

Primary Functions

  • Design, build, and maintain enterprise-grade dashboards and interactive reports using Power BI, Tableau, Looker, or equivalent BI platforms to provide clear, actionable insights for business stakeholders and executive leadership.
  • Write, optimize, and maintain complex SQL queries and stored procedures for data extraction, transformation, and reporting from relational and cloud data warehouses (e.g., Snowflake, Redshift, BigQuery, SQL Server).
  • Develop and enforce data models, dimensional schemas, and semantic layers that support self-service analytics, consistent KPI definitions, and scalable reporting across business units.
  • Lead end-to-end report development: gather requirements from stakeholders, translate business questions into analytical solutions, prototype visualizations, iterate on feedback, and deliver production-ready reports.
  • Build and maintain ETL/ELT workflows using tools such as Azure Data Factory, SSIS, Informatica, dbt, or equivalent; ensure reliable data ingestion, transformation, and lineage for reporting systems.
  • Define, socialize, and monitor key performance indicators (KPIs) and business metrics; document business logic and assumptions to ensure metric consistency across dashboards and teams.
  • Implement row-level security, role-based access controls, and governance practices within BI tools and data warehouses to ensure data privacy and compliance with policies.
  • Perform root-cause analysis and debug data inconsistencies by tracing metrics through source systems, ETL jobs, and semantic layers to preserve trust in analytics.
  • Collaborate closely with product, finance, sales, marketing, and operations teams to prioritize reporting needs, translate strategic questions into technical requirements, and measure program performance.
  • Automate routine reporting processes and scheduled dataset refreshes, ensuring high availability and timely distribution of reports and KPIs to stakeholders.
  • Monitor dashboard performance and optimize query performance, dataset refresh times, and visualization rendering to deliver fast, responsive user experiences.
  • Create and maintain detailed documentation: data dictionaries, report catalogs, model diagrams, and runbooks to support onboarding and knowledge transfer.
  • Mentor junior analysts and BI developers, conduct code and dashboard reviews, and promote best practices in data modeling, visualization standards, and query optimization.
  • Build predictive and exploratory analytics prototypes with Python, R, or ML/analytics frameworks where applicable; partner with data science teams to productionize models into BI workflows.
  • Partner with data engineering to prioritize data ingestion and enrichment tasks; provide requirements for new data sources, transformation logic, and schema design.
  • Conduct ad-hoc and exploratory analyses to answer urgent business questions, quantify impact, and support decision-making with clear, data-backed recommendations.
  • Ensure data quality by designing validation tests, monitoring data pipeline health, and implementing automated alerts for anomalies, missing data, or pipeline failures.
  • Lead BI tool evaluations, proof-of-concepts, and vendor management for platform upgrades, integrations, and licensing optimization.
  • Present insights and storytelling to senior leadership and cross-functional teams; craft slide decks and data narratives that synthesize complex analysis into strategic recommendations.
  • Estimate scope, effort, and timelines for BI projects; manage small-to-medium implementation projects, coordinate cross-functional contributors, and deliver on schedule.
  • Translate regulatory and compliance requirements (e.g., GDPR, HIPAA where relevant) into BI controls and data handling practices to mitigate risk.
  • Create and maintain performance benchmarking dashboards to track user adoption, report utilization, and ROI of BI initiatives.
  • Implement data cataloging and metadata management practices to make datasets discoverable, understood, and trusted across the organization.
  • Validate and reconcile third-party or vendor-supplied data against internal systems to ensure accuracy of external integrations and enrichment.

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.
  • Train business users on self-service BI capabilities, template usage, and visualization best practices.
  • Assist with budgeting, licensing, and cost monitoring for BI platforms and cloud data storage.
  • Participate in cross-functional data governance councils and help define data stewardship responsibilities.
  • Prototype new visualization techniques and dashboard templates to accelerate delivery and improve usability.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL proficiency: complex joins, window functions, CTEs, query performance tuning, and query plan understanding.
  • Expert-level experience with Power BI and/or Tableau: dashboard development, DAX/MDX, calculated fields, parameters, and best practices for visualization design.
  • Data modeling and dimensional design: star schemas, slowly changing dimensions, facts, and aggregate tables for high-performance reporting.
  • Experience with cloud data warehouses: Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse.
  • ETL/ELT tooling knowledge: dbt, Azure Data Factory, SSIS, Informatica, or equivalent pipeline orchestration tools.
  • Programming for analytics: Python or R for data transformation, automation, and exploratory analysis.
  • Familiarity with BI semantic layers, report caching strategies, and dataset optimization techniques.
  • Knowledge of data governance, metadata management, and data catalog solutions (e.g., Collibra, Alation).
  • Experience with version control (Git), CI/CD practices for analytics, and deployment of BI artifacts.
  • Understanding of security, access controls, and compliance requirements (PII handling, GDPR, HIPAA where applicable).
  • Proficiency with Excel for advanced analysis, pivoting, Power Query, and data validation.
  • Exposure to cloud platforms (AWS, Azure, GCP) and containerization/orchestration (optional but beneficial).

Soft Skills

  • Strong business acumen: ability to translate strategy into measurable KPIs and analytical use cases.
  • Excellent stakeholder management: build relationships, manage expectations, and influence cross-functional partners.
  • Clear communicator: explain technical concepts to non-technical audiences and present insights to executives.
  • Analytical problem solver: structure ambiguous problems, synthesize data, and recommend practical solutions.
  • Project management and prioritization: manage multiple concurrent workstreams and deliver on deadlines.
  • Attention to detail and ownership: ensure accuracy, reproducibility, and documentation of analyses.
  • Curiosity and continuous learning: stay current on BI trends, tools, and data practices.
  • Collaborative team player with mentoring experience and a focus on knowledge sharing.
  • Adaptability in fast-paced environments and comfort with ambiguity.
  • Ethical mindset and commitment to data privacy and responsible analytics.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Data Science, Business Analytics, MBA with analytics focus, or related advanced degree.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 3–7 years of experience in business intelligence, analytics, or data engineering roles; early-career candidates (2+ years) with strong tool proficiency may also be considered.

Preferred:

  • 5+ years building production BI solutions with a combination of SQL, Power BI/Tableau, and modern cloud data warehouse technologies.
  • Proven track record of delivering cross-functional BI projects, developing KPIs, and scaling analytics for business impact.
  • Prior experience in your industry (e.g., finance, retail, healthcare, SaaS) is a plus for domain-specific analytics.
Explore More Careers